Healthcare is an important aspect in everyday life, with quality and affordable care being essential for a population’s well-being and life expectancy. Even so, associated costs for medical services continue to rise. One aspect contributing to increased costs in healthcare is waste and fraud. In particular, with the rapidly rising elderly population in the United States, programs like Medicare are subject to high losses due to fraud. Therefore, fraud detection approaches are critical in lessening these losses. Even so, many studies using Medicare data do not provide sufficient details regarding data processing and/or integration making it potentially more difficult to understand the experimental results and challenging to reproduce the experiments. In this paper, we present current research using Medicare data to detect fraud, focusing on data processing and/or integration, and assess any gaps in the provided data-related details. We then present discussions on important details to look for when processing and merging different Medicare datasets indicating opportunities for future work.

A customized bundle is a list of products recommended to consumers among them they can choose his/her favorite products according to his/her preference. It is an efficient way to not only simplify the customer's shopping process, but also reduce the order fulfillment cost for the online supermarkets. A customized bundle recommendation method is proposed for online supermarket in this research. It is realized by combinational using association rule mining, customer segmentation and recommendation techniques. The association rules of product category are used to avoid mass unnecessary association rules of product. The product lists recommended within each category are generated by product ranking on each customer segmentation. Numerical experiments are conducted to verify the effectiveness of the proposed method. The method can be easily extended

In the recent years, social networks in business are gaining unprecedented popularity because of their potential for business growth. Companies can know more about consumers’ sentiments towards their products and services, and use it to better understand the market and improve their brand. Thus, companies regularly reinvent their marketing strategies and campaigns to fit consumers’ preferences. Social analysis harnesses and utilizes the vast volume of data in social networks to mine critical data for strategic decision making. It uses machine learning techniques and tools in determining patterns and trends to gain actionable insights. This paper selected a popular food brand to evaluate a given stream of customer comments on Twitter. Several metrics in classification and clustering of data were used for analysis. A Twitter API is used to collect twitter corpus and feed it to a Binary Tree classifier that will discover the polarity lexicon of English tweets, whether positive or negative. A k-means clustering technique is used to group together similar words in tweets in order to discover certain business value. This paper attempts to discuss the technical and business perspectives of text mining analysis of Twitter data and recommends appropriate future opportunities in developing this emerging field.

Cloud-based Personal Health Record systems (CB-PHR) have great potential in facilitating the management of individual health records. Security and privacy concerns are among the main obstacles for the wide adoption of CB-PHR systems. In this paper, we consider a multi-source CB-PHR system in which multiple data providers such as hospitals and physicians are authorized by individual data owners to upload their personal health data to an untrusted public cloud. The health data are submitted in an encrypted form to ensure data security, and each data provider also submits encrypted data indexes to enable queries over the encrypted data. We propose a novel Multi-Source Order-Preserving Symmetric Encryption (MOPSE) scheme whereby the cloud can merge the encrypted data indexes from multiple data providers without knowing the index content. MOPSE enables efficient and privacy-preserving query processing in that a data user can submit a single data query the cloud can process over the encrypted data from all related data providers without knowing the query content. We also propose an enhanced scheme, MOPSE+, to more efficiently support the data queries by hierarchical data providers. Extensive analysis and experiments over real datasets demonstrate the efficacy and efficiency of MOPSE and MOPSE+.

Considering the growing use of cloud computing and the need for optimal use of resources in the cloud, and attention to users that pay for services they use based on their pay-as-you-go basis, There should be a quicker way for users to decrease the user's waiting time and task's waiting time. The main purpose of this paper is to provide an optimal algorithm using the advantages of the two traditional Min-Min and Max- Min algorithms. The other point that follow in this algorithm (TOMMP) is the priority of the tasks. There are a lot of scheduling algorithms in the world today, but the priority given to the tasks has been neglected and overlooked in most algorithms. In this algorithm, priority is firstly selected for tasks based on a prioritization algorithm, and then using the median number to decide which one of the Min-Min or Max-Min algorithms is to be used. It should be noted that according to the TOMMP algorithms, its waiting time is lower than comparisons of the compared algorithms and is shown to be better than the comparable algorithms.

Cloud storage has been in widespread use nowadays, which alleviates users’ burden of local data storage. Meanwhile, how to ensure the security and integrity of the outsourced data stored in a cloud storage server has also attracted enormous attention from researchers. Proofs of storage (POS) is the main technique introduced to address this problem. Publicly verifiable POS allowing a third party to verify the data integrity on behalf of the data owner significantly improves the scalability of cloud service. However, most of existing publicly verifiable POS schemes are extremely slow to compute authentication tags for all data blocks due to many expensive group exponentiation operations, even much slower than typical network uploading speed, and thus it becomes the bottleneck of the setup phase of the POS scheme. In this article, we propose a new variant formulation called “Delegatable Proofs of Storage (DPOS)”. Then, we construct a lightweight privacy-preserving DPOS scheme, which on one side is as efficient as private POS schemes, and on the other side can support third party auditor and can switch auditors at any time, close to the functionalities of publicly verifiable POS schemes. Compared to traditional publicly verifiable POS schemes, we speed up the tag generation process by at least several hundred times, without sacrificing efficiency in any other aspect. In addition, we extend our scheme to support fully dynamic operations with high efficiency, reducing the computation of any data update to O(log n) and simultaneously only requiring constant communication costs. We prove that our scheme is sound and privacy preserving against auditor in the standard model. Experimental results verify the efficient performance of our scheme.

Cloud storage auditing schemes for shared data refer to checking the integrity of cloud data shared by a group of users. User revocation is commonly supported in such schemes, as users may be subject to group membership changes for various reasons. Previously, the computational overhead for user revocation in such schemes is linear with the total number of file blocks possessed by a revoked user. The overhead, however, may become a heavy burden because of the sheer amount of the shared cloud data. Thus, how to reduce the computational overhead caused by user revocations becomes a key research challenge for achieving practical cloud data auditing. In this paper, we propose a novel storage auditing scheme that achieves highly-efficient user revocation independent of the total number of file blocks possessed by the revoked user in the cloud. This is achieved by exploring a novel strategy for key generation and a new private key update technique. Using this strategy and the technique, we realize user revocation by just updating the nonrevoked group users’ private keys rather than authenticators of the revoked user. The integrity auditing of the revoked user’s data can still be correctly performed when the authenticators are not updated. Meanwhile, the proposed scheme is based on identity-base cryptography, which eliminates the complicated certificate management in traditional Public Key Infrastructure (PKI) systems. The security and efficiency of the proposed scheme are validated via both analysis and experimental results.

At present, there is a considerable increase in the amount of data stored in storage services, along with dramatic evolution of networking techniques. In storage services with huge data, the storage servers may want to reduce the volume of stored data, and the clients may want to monitor the integrity of their data with a low cost, since the cost of the functions related to data storage increase in proportion to the size of the data. To achieve these goals, secure deduplication and integrity auditing delegation techniques have been studied, which can reduce the volume of data stored in storage by eliminating duplicated copies and permit clients to efficiently verify the integrity of stored files by delegating costly operations to a trusted party, respectively. So far many studies have been conducted on each topic, separately, whereas relatively few combined schemes, which supports the two functions simultaneously, have been researched. In this paper, we design a combined technique which performs both secure deduplication of encrypted data and public integrity auditing of data. To support the two functions, the proposed scheme performs challengeresponse protocols using the BLS signature based homomorphic linear authenticator.We utilize a third party auditor for performing public audit, in order to help low-powered clients. The proposed scheme satisfies all the fundamental security requirements. We also propose two variances that provide higher security and better performance.

In the past few years, cloud computing develops very quickly. A large amount of data are uploaded and stored in remote public cloud servers which cannot fully be trusted by users. Especially, more and more enterprises would like to manage their data by the aid of the cloud servers. However, when the data outsourced in the cloud are sensitive, the challenges of security and privacy becomes urgent for wide deployment of the cloud systems. This paper proposes a secure data sharing scheme to ensure the privacy of data owner and the security of the outsourced cloud data. The proposed scheme provides flexible utility of data while solving the privacy and security challenges for data sharing. The security and efficiency analysis demonstrate that the designed scheme is feasible and efficient. At last, we discuss its application in E-health (electronic health) record.

In this paper, we propose an adaptive model of data storage in a heterogeneous distributed cloud environment. Our system utilizes the methods of secret sharing schemes and error correction codes based on Redundant Residue Number System (RRNS). We consider data uploading, storing and downloading. To minimize data access, we use data transfer mechanism between cloud providers. We provide theoretical analysis and experimental evaluation of our scheme with six real data storage providers. We show how dynamic adaptive strategies not only increase security, reliability, and reduction of data redundancy but allow processing encrypted data. We also discuss potentials of this approach, and address methods for mitigating the risks of confidentiality, integrity, and availability associated with the loss of information, denial of access for a long time, and information leakage.

Privacy of data in subjects of cloud computing or big data is one of the most principal issues. The privacy methods studied in previous research showed that privacy infringement for cloud computing or big data happened because multi risks on data by external or internal attackers. An important risk to take into consideration when speaking of the privacy of the stored transactions is represented by the transactions’ information which is not in the owner’s control. Such a case is represented by the cloud servers that are administered by cloud providers which cannot be wholly trusted by the users with sensitive, private data such as business plans or private information. A simple method for protecting data privacy is by applying certain privacy techniques onto transactions’ data, followed by the upload of the modified data into the cloud. In this paper, we are proposing a case study that is built on levels containing three models: cloud’s architecture, transaction’s manager and clients. Moreover, we consider that our case study is based on the premise of zero trust among the three models, therefore all the transactions take place with third-parties and the data movements are realized going through various levels of security.

Cloud-based Personal Health Record systems (CB-PHR) have great potential in facilitating the management of individual health records. Security and privacy concerns are among the main obstacles for the wide adoption of CB-PHR systems. In this paper, we consider a multi-source CB-PHR system in which multiple data providers such as hospitals and physicians are authorized by individual data owners to upload their personal health data to an untrusted public cloud. The health data are submitted in an encrypted form to ensure data security, and each data provider also submits encrypted data indexes to enable queries over the encrypted data. We propose a novel Multi-Source Order-Preserving Symmetric Encryption (MOPSE) scheme whereby the cloud can merge the encrypted data indexes from multiple data providers without knowing the index content. MOPSE enables efficient and privacy-preserving query processing in that a data user can submit a single data query the cloud can process over the encrypted data from all related data providers without knowing the query content. We also propose an enhanced scheme, MOPSE+, to more efficiently support the data queries by hierarchical data providers. Extensive analysis and experiments over real datasets demonstrate the efficacy and efficiency of MOPSE and MOPSE+.

Considering the growing use of cloud computing and the need for optimal use of resources in the cloud, and attention to users that pay for services they use based on their pay-as-you-go basis, There should be a quicker way for users to decrease the user's waiting time and task's waiting time. The main purpose of this paper is to provide an optimal algorithm using the advantages of the two traditional Min-Min and Max- Min algorithms. The other point that follow in this algorithm (TOMMP) is the priority of the tasks. There are a lot of scheduling algorithms in the world today, but the priority given to the tasks has been neglected and overlooked in most algorithms. In this algorithm, priority is firstly selected for tasks based on a prioritization algorithm, and then using the median number to decide which one of the Min-Min or Max-Min algorithms is to be used. It should be noted that according to the TOMMP algorithms, its waiting time is lower than comparisons of the compared algorithms and is shown to be better than the comparable algorithms.

Cloud storage has been in widespread use nowadays, which alleviates users’ burden of local data storage. Meanwhile, how to ensure the security and integrity of the outsourced data stored in a cloud storage server has also attracted enormous attention from researchers. Proofs of storage (POS) is the main technique introduced to address this problem. Publicly verifiable POS allowing a third party to verify the data integrity on behalf of the data owner significantly improves the scalability of cloud service. However, most of existing publicly verifiable POS schemes are extremely slow to compute authentication tags for all data blocks due to many expensive group exponentiation operations, even much slower than typical network uploading speed, and thus it becomes the bottleneck of the setup phase of the POS scheme. In this article, we propose a new variant formulation called “Delegatable Proofs of Storage (DPOS)”. Then, we construct a lightweight privacy-preserving DPOS scheme, which on one side is as efficient as private POS schemes, and on the other side can support third party auditor and can switch auditors at any time, close to the functionalities of publicly verifiable POS schemes. Compared to traditional publicly verifiable POS schemes, we speed up the tag generation process by at least several hundred times, without sacrificing efficiency in any other aspect. In addition, we extend our scheme to support fully dynamic operations with high efficiency, reducing the computation of any data update to O(log n) and simultaneously only requiring constant communication costs. We prove that our scheme is sound and privacy preserving against auditor in the standard model. Experimental results verify the efficient performance of our scheme.

At present, there is a considerable increase in the amount of data stored in storage services, along with dramatic evolution of networking techniques. In storage services with huge data, the storage servers may want to reduce the volume of stored data, and the clients may want to monitor the integrity of their data with a low cost, since the cost of the functions related to data storage increase in proportion to the size of the data. To achieve these goals, secure deduplication and integrity auditing delegation techniques have been studied, which can reduce the volume of data stored in storage by eliminating duplicated copies and permit clients to efficiently verify the integrity of stored files by delegating costly operations to a trusted party, respectively. So far many studies have been conducted on each topic, separately, whereas relatively few combined schemes, which supports the two functions simultaneously, have been researched. In this paper, we design a combined technique which performs both secure deduplication of encrypted data and public integrity auditing of data. To support the two functions, the proposed scheme performs challenge response protocols using the BLS signature based homomorphic linear authenticator.We utilize a third party auditor for performing public audit, in order to help low-powered clients. The proposed scheme satisfies all the fundamental security requirements. We also propose two variances that provide higher security and better performance.

In the past few years, cloud computing develops very quickly. A large amount of data are uploaded and stored in remote public cloud servers which cannot fully be trusted by users. Especially, more and more enterprises would like to manage their data by the aid of the cloud servers. However, when the data outsourced in the cloud are sensitive, the challenges of security and privacy becomes urgent for wide deployment of the cloud systems. This paper proposes a secure data sharing scheme to ensure the privacy of data owner and the security of the outsourced cloud data. The proposed scheme provides flexible utility of data while solving the privacy and security challenges for data sharing. The security and efficiency analysis demonstrate that the designed scheme is feasible and efficient. At last, we discuss its application in E-health (electronic health) record.

In this paper, we propose an adaptive model of data storage in a heterogeneous distributed cloud environment. Our system utilizes the methods of secret sharing schemes and error correction codes based on Redundant Residue Number System (RRNS). We consider data uploading, storing and downloading. To minimize data access, we use data transfer mechanism between cloud providers. We provide theoretical analysis and experimental evaluation of our scheme with six real data storage providers. We show how dynamic adaptive strategies not only increase security, reliability, and reduction of data redundancy but allow processing encrypted data. We also discuss potentials of this approach, and address methods for mitigating the risks of confidentiality, integrity, and availability associated with the loss of information, denial of access for a long time, and information leakage.

Privacy of data in subjects of cloud computing or big data is one of the most principal issues. The privacy methods studied in previous research showed that privacy infringement for cloud computing or big data happened because multi risks on data by external or internal attackers. An important risk to take into consideration when speaking of the privacy of the stored transactions is represented by the transactions’ information which is not in the owner’s control. Such a case is represented by the cloud servers that are administered by cloud providers which cannot be wholly trusted by the users with sensitive, private data such as business plans or private information. A simple method for protecting data privacy is by applying certain privacy techniques onto transactions’ data, followed by the upload of the modified data into the cloud. In this paper, we are proposing a case study that is built on levels containing three models: cloud’s architecture, transaction’s manager and clients. Moreover, we consider that our case study is based on the premise of zero trust among the three models, therefore all the transactions take place with third-parties and the data movements are realized going through various levels of security

<p> Cloud computing is a very useful solution to many individual users and organizations. It can provide many services based on different needs and requirements. However, there are many issues related to the user data that need to be addressed when using cloud computing. Among the most important issues are: data ownership, data privacy, and storage. The users might be satisfied by the services provided by the cloud computing service providers, since they need not worry about the maintenance and storage of their data. On the other hand, they might be worried about unauthorized access to their private data. Some solutions to these issues were proposed in the literature, but they mainly increase the cost and processing time since they depend on encrypting the whole data. In this paper, we are introducing a cloud computing framework that classifies the data based on their importance. In other words, more important data will be encrypted with more secure encryption algorithm and larger key sizes, while less important data might even not be encrypted. This approach is very helpful in reducing the processing cost and complexity of data storage and manipulation since we do not need to apply the same sophisticated encryption techniques to the entire users data. The results of applying the proposed framework show improvement and efficiency over other existing frameworks. </p>

System Architecture

With the popularity of wearable devices, along with the development of clouds and cloudlet technology, there has been increasing need to provide better medical care. The processing chain of medical data mainly includes data collection, data storage and data sharing, etc. Traditional healthcare system often requires the delivery of medical data to the cloud, which involves users’ sensitive information and causes communication energy consumption. Practically, medical data sharing is a critical and challenging issue. Thus in this paper, we build up a novel healthcare system by utilizing the flexibility of cloudlet. The functions of cloudlet include privacy protection, data sharing and intrusion detection. In the stage of data collection, we first utilize Number Theory Research Unit (NTRU) method to encrypt user’s body data collected by wearable devices. Those data will be transmitted to nearby cloudlet in an energy efficient fashion. Secondly, we present a new trust model to help users to select trustable partners who want to share stored data in the cloudlet. The trust model also helps similar patients to communicate with each other about their diseases. Thirdly, we divide users’ medical data stored in remote cloud of hospital into three parts, and give them proper protection. Finally, in order to protect the healthcare system from malicious attacks, we develop a novel collaborative intrusion detection system (IDS) method based on cloudlet mesh, which can effectively prevent the remote healthcare big data cloud from attacks. Our experiments demonstrate the effectiveness of the proposed scheme.

System Architecture

Project Overview
Cloud1, cloud2, doctor(user) login to access the data. Once the attacker attacker the data will not sent directly and bypass by intermediate cloud to send the data. Cloud1 and Cloud2 can share that sensitive data (doctor too).

System Requirement

Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse

Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : core java, web service
IDE : Netbeans.

With the maturity of service-oriented architecture (SOA) and Web technologies, web services have become critical components of Software as a Service (SaaS) applications in cloud ecosystem environments. Most SaaS applications leverage multi-tenant data stores as a back end to keep and process data with high agility. Although these technologies promise impressive benefits, they put SaaS applications at risk against novel as well as prevalent attack vectors. This security risk is further magnified by the loss of control and lack of security enforcement over sensitive data manipulated by SaaS applications. An effective solution is needed to fulfill several requirements originating in the dynamic and complex nature of such applications. Inspired by the rise of Security as a Service (SecaaS) model, this paper introduces “Information Flow Control as a Service ()”.  lays the foundation of cloud-delivered IFC-based security analysis and monitoring services. As an example of the adoption of the , this paper presents a novel framework that addresses the detection of information flow vulnerabilities in SaaS applications. Our initial experiments show that the framework is a viable solution to protect against data integrity and confidentiality violations leading to information leakage.

System Architecture

Project:Overview
Three clouds will be maintained to upload the data and if attacker comes in data will be blocked. If the downloading happening cloud will check the vulnerability and send back the data with accuracy of vulnerability.

System Requirement

Hardware Requirement Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse

Software Requirement Operating System : Windows xp,7,8
Front End : Java 7
Technology : core java, web service
IDE : Netbeans


Below code is for uploading the file to data center
Alert_Service service = new Alert_Service();
String hibFileName = "D:\\cloud_data\\hibernate\\"+fileName;
Alert port = service.getAlertPort();
port.hello(finalFile+"#"+"hibernate");
toReturn = "uploaded to hibernat datacenter";


<

With the advent of cloud computing, it has become increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve this data. For privacy concerns, secure searches over encrypted cloud data has motivated several research works under the single owner model. However, most cloud servers in practice do not just serve one owner; instead, they support multiple owners to share the benefits brought by cloud computing. In this paper, we propose schemes to deal with -keyword Search in a Multi-owner model (PRMSM). To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel Additive Order and Privacy Preserving Function family. To prevent the attackers from eavesdropping secret keys and pretending to be legal data users submitting searches, we propose a novel dynamic secret key generation protocol and a new data user authentication protocol. Furthermore, PRMSM supports efficient data user revocation.Extensive experiments on real-world datasets confirm the efficacy and efficiency of PRMSM.

System Architecture


Cloud , owner and user , owner can send the files to cloud,owner can extract the keywords and user can send the keyword and cloud will find the top 3 matching keywords and gets decrypt and will be downloaded.

System Requirement

Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse


Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings
IDE : Eclipse.
Database : Oracle 10g.

Below code is used to create a bucket in he amazon cloud where the uploaded files to cloud will be stored

AmazonS3 s3 = new AmazonS3Client(new PropertiesCredentials(
n1bucket.class.getResourceAsStream("AwsCredentials.properties")));
System.out.println("===========================================");
System.out.println("Getting Started with Amazon S3")
System.out.println("===========================================\n");
s3.createBucket(response);

Cloud computing is the latest technology in the field of distributed computing. It provides various online and on-demand services for data storage, network services, platform services and etc. Many organizations are unenthusiastic to use cloud services due to data security issues as the data resides on the cloud services provider’s servers. To address this issue, there have been several approaches applied by various researchers worldwide to strengthen security of the stored data on cloud computing. The Bi-directional DNA Encryption Algorithm (BDEA) is one such data security techniques. However, the existing technique focuses only on the ASCII character set, ignoring the non-English user of the cloud computing. Thus, this proposed work focuses on enhancing the BDEA to use with the Unicode characters

System Architecture

Cloud and client with intermediatory node which will act as bridge. Client tries upload the files DNA crytography can be done to send the file data and once downloads by the client intermediator node will decrypt and sends the data to client.

System Requirement

Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse

Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings,Networking.
IDE : Eclipse.

Below code is to open file dialogue box for selecting file to upload & to calculate the size of the file

FileDialog fd=new FileDialog(this,"Open",FileDialog.LOAD);
fd.show();
FileInputStream fin=new FileInputStream(fd.getDirectory()+fd.getFile());
jTextField1.setText(fd.getFile());
System.out.println("Select File"+fd.getFile());
File f = new File(fd.getDirectory()+fd.getFile());
fin.read(filebyte);
flen=(int)f.length(); file_string=new String(filebyte, "UTF-8").substring(0,flen);
t1.setText(file_string);
jTextArea1.setText("\n File Loaded");

<p>

Decentralized storage system for accessing data with anonymous authentication provides more secure user authentication, user revocation and prevents replay attacks. Access control is processed on decentralized KDCs it is being more secure for data encryption. Generated decentralized KDC's are then grouped by (KGC). Our system provides authentication for the user, in which only system authorized
users are able to decrypt, view the stored information. User validations and access control scheme are introduced in decentralized, which is useful for preventing replay attacks and supports modification of data stored in the cloud. The access control scheme is gaining more attention because it is important that only approved users have access to valid examine. Our scheme prevents supports creation, replay
attacks, reading and modify data stored in the cloud. We also address user revocation. The problems of validation, access control, privacy protection should be solved simultaneously. 

Cloud , trusted sub cloud and client to interact with cloud. Once the files are selected only valid users can download and other users can be blocked to access those files.

System Configuration

Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse


Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings,Networking.
IDE : Eclipse.
Database : Oracle 10g

Code for connecting amazon cloud
AmazonS3 s3 = new AmazonS3Client(new PropertiesCredentials(
n1bucket.class.getResourceAsStream("AwsCredentials.properties")));
System.out.println("===========================================");
System.out.println("Getting Started with Amazon S3")
System.out.println("===========================================\n");
s3.createBucket(response);

Performance of streaming applications are significantly impacted by the deployment decisions made at infrastructure level, i.e., number and configuration of resources allocated for each functional unit of the application. The current deployment practices are mostly platform-oriented, meaning that the deployment configuration is tuned to a static resource-set environment and thus is inflexible to use in cloud with an on-demand resource pool. In this paper, we propose P-Deployer, a deployment framework that enables streaming applications to run on IaaS clouds with satisfactory performance and minimal resource consumption. It achieves performance-oriented, cost-efficient and automated deployment by holistically optimizing the decisions of operator parallelization, resource provisioning, and task mapping. Using a Monitor-Analyze-Plan-Execute (MAPE) architecture, P-Deployer iteratively builds the connection between performance outcome and resource consumption through task profiling and models the deployment problem as a bin-packing variant. Extensive experiments using both synthetic and real-world streaming applications have shown the correctness and scalability of our approach, and demonstrated its superiority compared to platform-oriented methods in terms of resource cost.

Smart Grid (SG) technology represents an unprecedented opportunity to transfer the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. On the other hand, the emergence of Electric Vehicles (EVs) promises to yield multiple benefits to both power and transportation industry sectors, but it is also likely to affect the SG reliability, by consuming massive energy. Nevertheless, the plug-in of EVs at public supply stations must be controlled and scheduled in order to reduce the peak load. This paper considers the problem of plug-in EVs at public supply stations (EVPSS). A new communication architecture for smart grid and cloud services is introduced. Scheduling algorithms are proposed in order to attribute priority levels and optimize the waiting time to plug-in at each EVPSS. To the best of our knowledge, this is one of the first papers investigating the aforementioned issues using new network architecture for smart grid based on cloud computing. We evaluate our approach via extensive simulations and compare it with two other recently proposed works, based on real supply energy scenario in Toronto. Simulation results demonstrate the effectiveness of the proposed approach when considering real EVs charging-discharging loads at peak-hours periods.

In a recent paper, Ghazizadeh et al. have studied vehicular clouds running on top of the vehicles in the parking lot of a major airport. The defining difference between vehicular clouds and their conventional counterparts is the unpredictable availability of computational resources. Indeed, as vehicles enter the parking lot, fresh compute resources become available; when vehicles depart, their compute resources leave with them. In such a volatile environment, the task of promoting reliability becomes quite challenging. To solve the reliability problem, Ghazizadeh et al. suggested employing redundancy-based job assignment strategies. They derived analytical expressions for the mean time to failure of these strategies. Their expressions require full knowledge of the distribution of vehicle residency times and of the time it takes to recruit a vehicle into the vehicular cloud. In a practical context, the datacenter manager does not know these distribution functions. Instead, using accumulated empirical evidence, she may know the first and perhaps the second moment of these random variables. With this in mind, this paper derives easy-to-compute approximations of the mean time to failure of the job assignment strategies proposed by Ghazizadeh et al.. A comprehensive set of simulations have shown that our approximations are very close to the analytical predictions by Ghazizadeh et al. even if the exact distribution functions are not known.

Information resource services are the key element for resource sharing in cloud manufacturing. Traditional resource service models focus on modelling the attributes, interfaces and descriptions of the resources into resource information services. Such resource services are suitable for local environment but suffer semantic heterogeneities in open could environment. Recently, well designed ontologies are applied in resource service models to unify the schemas and eliminate the semantic heterogeneities among the services. However, the effectiveness of ontology-based models mainly depends on the expertises of the ontology experts in ontology designing. Moreover, it is difficult to catch the dynamic changes in the cloud once the ontology has been embedded. In this paper, a semantic model is presented for information resource service modelling that uses semantic links instead of ontologies. The model takes advantage of semantic links to enable automated integrating and distributed updating in resource service cloud. In the experiment, the model is applied on practical manufacturing resources from a wheel manufacturing company. The case study and experimental results show that the proposed model is suitable for modelling manufacturing resources into cloud services and enables the flexible and distributed manipulation on resource services in the cloud environment.

The substantial growth of the distributed computing using heterogeneous computing has enabled great expansions in Cyber Physical Systems (CPS). Combining CPS with heterogeneous cloud computing is an alternative approach for increasing sustainability of the system. However, execution of resource management in cloud systems is still encountering a few challenges, including the bottlenecks of the Web server capacities and task assignments in the heterogeneous cloud. The unstable service demands often result in service delays, which embarrasses the competitiveness of the enterprises. This paper addresses the problem of the task assignment in heterogeneous clouds, which is proved as a NP-hard problem. The proposed approach is called Smart Cloud-based Optimizing Workload (SCOW) Model that uses predictive cloud capacities and considers sustainable factors to assign tasks to heterogeneous clouds. To reach the optimization objective, we propose a few algorithms, which include Workload Resource Minimization Algorithm (WRM), Smart Task Assignment (STA) Algorithm, and Task Mapping Algorithm (TMA). Our experimental evaluations have examined the performance of the proposed scheme.

Advances in computation, sensing, and networking have led to interest in the Internet of things (IoT) and cyberphysical systems (CPS). Developments concerning the IoT and CPS will improve critical infrastructure, vehicle networks, and personal health products. Unfortunately, these systems are vulnerable to attack. Advanced persistent threats (APTs) are a class of long-term attacks in which well-resourced adversaries infiltrate a network and use obfuscation to remain undetected. In a CPS under APTs, each device must decide whether to trust other components that may be compromised. In this paper, we propose a concept of trust (strategic trust) that uses game theory to capture the adversarial and strategic nature of CPS security. Specifically, we model an interaction between the administrator of a cloud service, an attacker, and a device that decides whether to trust signals from the vulnerable cloud. Our framework consists of a simultaneous signaling game and the FlipIt game. The equilibrium outcome in the signaling game determines the incentives in the FlipIt game. In turn, the equilibrium outcome in the FlipIt game determines the prior probabilities in the signaling game. The Gestalt Nash equilibrium (GNE) characterizes the steady state of the overall macro-game. The novel contributions of this paper include proofs of the existence, uniqueness, and stability of the GNE. We also apply GNEs to strategically design a trust mechanism for a cloud-assisted insulin pump. Without requiring the use of historical data, the GNE obtains a risk threshold beyond which the pump should not trust messages from the cloud. Our framework contributes to a modeling paradigm called games-of-games.

Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms.

Cloud computing is expected to be a promising solution for scientific computing. In this paper, we propose a flexible container-based computing platform to run scientific workflows on cloud. We integrate Galaxy, a popular biology workflow system, with four famous container cluster systems. Preliminary evaluation shows that container cluster systems introduce negligible performance overhead for data intensive scientific workflows, meanwhile, they are able to solve tool installation problem, guarantee reproducibility and improve resource utilization. Moreover, we implement four ways of using Docker, the most popular container tool, for our platform. Docker in Docker and Sibling Docker, which run everything within containers, both help scientists easily deploy our platform on any clouds in a few minutes.

Machine learning (ML) approach to modeling and predicting real-world dynamic system behaviours has received widespread research interest. While ML capability in approximating any nonlinear or complex system is promising, it is often a black-box approach, which lacks the physical meanings of the actual system structure and its parameters, as well as their impacts on the system. This paper establishes a model to provide explanation on how system parameters affect its output(s), as such knowledge would lead to potential useful, interesting and novel information. The paper builds on our previous work in ML, and also combines an evolutionary artificial neural networks with sensitivity analysis to extract and validate key factors affecting the cloud data center energy performance. This provides an opportunity for software analysts to design and develop energy-aware applications and for Hadoop administrator to optimize the Hadoop infrastructure by having Big Data partitioned in bigger chunks and shortening the time to complete MapReduce jobs.

Cloud platforms offer computing, storage and other related resources to cloud consumers in the form of Virtual Machines (VMs), and allow VMs scaling according to the workload characteristic. Specially, with cloud computing, service providers need no longer to maintain a large number of expensive physical machines, which can significantly reduce the cost. However, it is still a challenge for service providers to purchase the optimal number of VMs from distributed clouds due to the uncertainty of the service demands and the operational cost. To address this problem, in this paper, a Cost-efficient Provisioning strategy for Multiple concurrent Services (CPMS) in distributed clouds is proposed by formulating and solving a two-stage stochastic programming model. The objective of this model is to minimize the resource cost of purchasing VMs in the first stage and maximize the expected profit in the second stage. Due to the large number of system states (scenarios) in the environment with multiple services and distributed clouds, the sample average approximation is applied to solve the proposed stochastic programming. Finally, the experiments are carried out based on real workload traces to show the attainable performance of the proposed strategy.

This paper proposes an anomalous behavior detection model based on cloud computing. Virtual Machines (VMs) are one of the key components of cloud Infrastructure as a Service (IaaS). The security of such
VMs is critical to IaaS security. Many studies have been done on cloud computing security issues, but research into VM security issues, especially regarding VM network traffic anomalous behavior detection, remains inadequate. More and more studies show that communication among internal nodes exhibits complex patterns. Communication among VMs in cloud computing is invisible. Researchers find such issues challenging, and few solutions have been proposed—leaving cloud computing vulnerable to network attacks. This paper proposes a model that uses Software-Defined Networks (SDN) to implement traffic redirection. Our model can capture inter-VM traffic, detect known and unknown anomalous network behaviors, adopt hybrid techniques to analyze VM network behaviors, and control network systems. The experimental results indicate that the effectiveness of our approach is greater than 90%, and prove the feasibility of the model.

Cloud storage services enable users to outsource their data to cloudservers and access that data remotely over the Internet. These services give users an efficient and flexible way to manage their data
without deploying and maintaining local storage devices and services.1–4 Specifically, users can process their data on their PCs, outsource the processed data to cloud servers, and use the data on other devices
 

However, despite the benefits brought by cloud storage services, critical security concerns in data outsourcing exist. One of the most important security concerns for users is data integrity—that is, whether their data remains intact on cloud servers. 7 A cloud service provider might hide data loss incidents to maintain its reputation8 or discard data that’s rarely accessed to save storage space, while claiming that no data loss has occurred. Moreover, an external adversary might distort users’ data on cloud servers for financial or political reasons. Consequently, users require an efficient and secure verification method to ensure their data’s integrity.

 

Existing software-based data erasure programs can be summarized as following the same one-bit-return protocol: the deletion program performs data erasure and returns either success or failure. However, such a onebit- return protocol turns the data deletion system into a black box – the user has to trust the outcome but cannot easily verify it. This is especially problematic when the deletion program is encapsulated within a Trusted Platform Module (TPM), and the user has no access to the code inside. In this paper, we present a cryptographic solution that aims to make the data deletion process more transparent and verifiable. In contrast to the conventional black/white assumptions about TPM (i.e., either completely trust or distrust), we introduce a third assumption that sits in between: namely, “trust-but-verify”. Our solution enables a user to verify the correct implementation of two important operations inside a TPM without accessing its source code: i.e., the correct encryption of data and the faithful deletion of the key. Finally, we present a proof-of-concept implementation of the SSE system on a resource-constrained Java card to demonstrate its practical feasibility. To our knowledge, this is the first systematic solution to the secure data deletion problem based on a “trust-but-verify” paradigm, together with a concrete prototype implementation..

This paper reveals an intrinsic relationship between secure cloud storage and secure network coding for the first time. Secure cloud storage was proposed only recently while secure network coding has been studied for more than ten years. Although the two areas are quite different in their nature and are studied independently, we show how to construct a secure cloud storage protocol given any secure network coding protocol. This gives rise to a systematic way to construct secure cloud storage protocols. Our construction is secure under a definition which captures the real world usage of the cloud storage. Furthermore, we propose two specific secure cloud storage protocols based on two recent secure network coding protocols. In particular, we obtain the first publicly verifiable secure cloud storage protocol in the standard model. We also enhance the proposed generic construction to support user anonymity and third-party public auditing, which both have received considerable attention recently. Finally, we prototype the newly proposed protocol and evaluate its performance. Experimental results validate the effectiveness of the protocol

In cloud computing datacentersexert server unification to enhance the efficiency of resources. Many Vms (virtual machine) are running on each datacenter to utilize the resources efficiently. Most of the time cloud resources are underutilized due to poor scheduling of task (or application) in datacenter. In this paper, we propose a multi-objective task scheduling algorithm formappingtasks to a Vms in order to improve the throughput of the datacenter and reduce the cost without violating the SLA (Service Level Agreement) for an application in cloud SaaS environment. The proposed algorithm provides an optimal scheduling method. Most of the algorithms schedule tasks based on single criteria (i.e execution time). But in cloud environment it is required to consider various criteria like execution time, cost, bandwidth of user etc. This algorithm is simulated using CloudSim simulator and the result shows better performance and improved throughput.

Cloud computing provides a flexible and convenient way for data sharing, which brings various benefits for both the society and individuals. But there exists a natural resistance for users to directly outsource the shared data to the cloud server since the data often contain valuable information. Thus, it is necessary to place cryptographically enhanced access control on the shared data. Identity-based encryption is a promising cryptographical primitive to build a practical data sharing system. However, access control is not static. That is, when some user’s authorization is expired, there should be a mechanism that can remove him/her from the system. Consequently, the revoked user cannot access both the previously and subsequently shared data. To this end, we propose a notion called revocable-storage identity-based encryption (RS-IBE), which can provide the forward/backward security of ciphertext by introducing the functionalities of user revocation and ciphertext update simultaneously. Furthermore, we present a concrete construction of RS-IBE, and prove its security in the defined security model. The performance comparisons indicate that the proposed RS-IBE scheme has advantages in terms of functionality and efficiency, and thus is feasible for a practical and cost-effective data-sharing system. Finally, we provide implementation results of the proposed scheme to demonstrate its practicability.

Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing.To better protect data security, this paper makes the first attempt to formally address the problem of authorized data deduplication. Different from traditional deduplication systems, the differential privileges of users are further considered in duplicate check besides the data itself. We also present several new deduplication constructions supporting authorized duplicate check in a hybrid cloud architecture. Security analysis demonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement a prototype of our proposed authorized duplicate check scheme and conduct testbed experiments using our prototype. We show that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations.

Data access control has becoming a challenging issue in cloud storage systems. Some techniques have been proposed to achieve the secure data access control in a semitrusted cloud storage system. Recently, K.Yang et al.proposed a basic data access control scheme for multiauthoritycloud storage system (DAC-MACS) and an extensive data access control scheme (EDAC-MACS). They claimed that the DAC-MACS could achieve efficient decryption and immediate revocation and the EDAC-MACS could also achieve these goals even thoughnonrevoked users reveal their Key Update Keys to the revoked user. However, through our cryptanalysis, the revocation security of both schemes cannot be guaranteed. In this paper, we first give two attacks on the two schemes. By the first attack, the revoked user can eavesdrop to obtain other users’ Key Update Keys to update its Secret Key, and then it can obtain proper Token to decrypt any secret information as a nonrevoked user. In addition, by the second attack, the revoked user can intercept Ciphertext Update Key to retrieve its ability to decrypt any secret information as a nonrevoked user. Secondly, we propose a new extensive DAC-MACS scheme (NEDAC-MACS) to withstand the above two attacks so as to guarantee more secure attribute revocation. Then, formal cryptanalysis of NEDAC-MACS is presented to prove the security goals of the scheme. Finally, the performance comparison among NEDAC-MACS and related schemesisgivento demonstrate that the performance of NEDAC-MACS is superior to that of DACC, and relatively same as that of DAC-MACS.

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to reauthenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Wireless Cloud computing delivers the data and computing resources through the internet, on a pay for usage basis. By using this, we can automatically update our software. We can use only the space required for the server, which reduces the carbon footprint. Task scheduling is the main problem in cloud computing which reduces the system performance. To improve system performance, there is need of an efficient task-scheduling algorithm. Existing task-scheduling algorithms focus on taskresource requirements, CPU memory, execution time and execution cost. However, these do not consider network bandwidth. In this paper, we introduce an efficient taskscheduling algorithm, which presents divisible task scheduling by considering network bandwidth. By this, we can allocate the workflow based on the availability of network bandwidth. Our proposed task-scheduling algorithm uses a nonlinear programming model for divisible task scheduling, which assigns the correct number of tasks to each virtual machine. Based on the allocation, we design an algorithm for divisible load scheduling by considering the network bandwidth.

Cloud provides convenient and on demand network access for computing resources available over internet. Individuals and organizations can access the software and hardware such as network, storage, server and applications which are located remotely easily with the help of Cloud Service. The tasks/jobs submitted to this cloud environment needs to be  executed on time using the resources available so as to achieve proper resource utilization, efficiency and lesser makespan which in turn requires efficient task scheduling algorithm for proper task allocation. In this paper, we have introduced an Optimized Task Scheduling Algorithm which adapts the advantages of various other existing algorithms according to thesituation while considering the distribution and scalability characteristics of cloud resources.

In most existing CP-ABE schemes, there is only one authority in the system and all the public keys and private keys are issued by this authority, which incurs ciphertext size and computation costs in the encryption and decryption operations that depend at least linearly on the number of attributes involved in the access policy. We propose an efficient multi-authority CP-ABE scheme in which the authorities need not interact to generate public information during the system initialization phase. Our scheme has constant ciphertext length and a constant number of pairing computations. Our scheme can be proven CPA-secure in random oracle model under the decision q-BDHE assumption. When user’s attributes revocation occurs, the scheme transfers most re-encryption work to the cloud service provider, reducing the data owner’s computational cost on the premise of security. Finally the analysis and simulation result show that the schemes proposed in this thesis ensure the privacy and secure access of sensitive data stored in the cloud server, and be able to cope with the dynamic changes of users’ access privileges in large-scale systems. Besides, the multi-authority ABE eliminates the key escrow problem, achieves the length of ciphertext optimization and enhances the efficiency of the encryption and decryption operations

Mobile Ad hoc Network (MANET) has been pervasive in many applications, including some procedures such as security in critical applications has been a major  threats in MANETs. This exceptional characteristic of MANETs, anticipation methodologies lonely cannot able to be secure the data. In this circumstance secure acknowledgment of each data should have a defensive force before the attackers violate the system. The mechanism of Intrusion Detection System (IDS) is normally used to protect the wireless networks for security purposes in MANETs. In case of MANETs, intrusion detection system is favored since the first day of their invention. Communication is restricted to the transmitters within a radio frequency range. Owing to the superior technology that reduces the cost of infrastructure services to gain more importance in autonomous topology of mobile nodes. A novel IDS, EAACK is mainly a secure authentication method using acknowledgment for MANETs to transmit packets in mobility nodes. In this case, out of range in mobile nodes cases security issues while transmitting data from source to destination nodes. This results that the communication of each mobility nodes takes place in radio frequency range and the out of range in communication leads the parties to relay data transmissions to reach the destination node.

Statistics from security firms, research institutions and government organizations show that the number of data-leak instances have grown rapidly in recent years. Among various data-leak cases, human mistakes are one of the main causes of data loss. There exist solutions detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. A common approach is to screen  content in storage and transmission for exposed sensitive information. Such an approach usually requires the detection operation to be conducted in secrecy. However, this secrecy requirement is challenging to satisfy in practice, as detection servers may be compromised or outsourced. In this paper, we present a privacy- preserving data-leak detection (DLD) solution to solve the issue where a special set of sensitive data digests is used in detection.The advantage of our method is that it enables the data owner to safely delegate the detection operation to a semihonest provider without revealing the sensitive data to the provider. We describe how Internet service providers can offer their customers DLD as an add-on service with strong privacy guarantees. The evaluation results show that our method can support accurate detectionwith very small number of false alarms under various data-leakscenarios.

Recent years have witnessed the trend of leveraging cloud-based services for large scale content storage, processing, and distribution. Security and privacy are among top concerns for the public cloud environments. Towards these security challenges, we propose and implement, on Open Stack Swift, a new client-side de duplication scheme for securely storing and sharing outsourced data via the public cloud. The originality of our proposal is twofold. First, it ensures better confidentiality towards unauthorized users. That is, every client computes a per data key to encrypt the data that he intends to store in the cloud. As such, the data access is managed by the data owner. Second, by integrating access rights in metadata file, an authorized user can decipher an encrypted file only with his private key.

As the cloud computing technology develops during the last decade, outsourcing data to cloud service for storage becomes an attractive trend, which benefits in sparing efforts on heavy data maintenance and management. Nevertheless, since the outsourced cloud storage is not fully trustworthy, it raises security concerns on how to realize data deduplication in cloud while achieving integrity auditing. In this work, we study the problem of integrity auditing and secure deduplication on cloud data. Specifically, aiming at achieving both data integrity and deduplication in cloud, we propose two secure systems, namely SecCloud and SecCloud+. SecCloud introduces an auditing entity with a maintenance of a MapReduce cloud, which helps clients generate data tags before uploading as well as audit the integrity of data having been stored in cloud. Compared with previous work, the computation by user in SecCloud is greatly reduced during the file uploading and auditing phases. SecCloud+ is designed motivated by the fact that customers always want to encrypt their data before uploading, and enables integrity auditing and secure deduplication on encrypted data.

Data access control is an effective way to ensure the data security in the cloud. Due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Ciphertext-Policy Attribute-based Encryption (CP-ABE) is regarded as one of the most suitable technologies for data access control in cloud storage, because it gives data owners more direct control on access policies. However, it is difficult to directly apply existing CP-ABE schemes to data access control for cloud storage systems because of the attribute revocation problem. In this paper, we design an expressive, efficient and revocable data access control scheme for multi-authority cloud storage systems, where there are multiple authorities co-exist and each authority is able to issue attributes independently. Specifically, we propose a revocable multi-authority CP-ABE scheme, and apply it as the underlying techniques to design the data access control scheme. Our attribute revocation method can efficiently achieve both forward security and backward security. The analysis and simulation results show that our proposed data access control scheme is secure in the random oracle model and is more efficient than previous works.

Recently, a public auditing protocol for shared data called Panda (IEEE Transactions on Services Computing, doi:10.1109/TSC.2013.2295611) was proposed to ensure the correctness of the outsourced data. A distinctive feature of Panda is the support of data sharing and user revocation. Unfortunately, in this letter, we show that Panda is insecure in the sense that a cloud server can hide data loss without being detected. Specifically, we show that even some stored file blocks have been lost, the server is able to generate a valid proof by replacing a pair of lost data block and its signature with another block and signature pair. We also provide a solution to the problem while preserving all the desirable features of the original protocol

Induced by incorporating the powerful data storage and data processing abilities of cloud computing (CC) as well as ubiquitous data gathering capability of wireless sensor networks (WSNs), CC-WSN integration received a lot of attention from both academia and industry. However, authentication as well as trust and reputation calculation and management of cloud service providers (CSPs) and sensor network providers (SNPs) are two very critical and barely explored issues for this new paradigm. To fill the gap, this paper proposes a novel  authenticated trust and reputation calculation and management (ATRCM) system for CC-WSN integration. Considering the authenticity of CSP and SNP, the attribute requirement of cloud service user (CSU) and CSP, the cost, trust, and reputation of the service of CSP and SNP, the proposed ATRCM system achieves the following three functions: 1) authenticating CSP and SNP to avoid malicious impersonation attacks; 2) calculating and managing trust and reputation regarding the service of CSP and SNP; and 3) helping CSU choose desirable CSP and assisting CSP in selecting appropriate SNP. Detailed analysis and design as well as further functionality evaluation results are presented to demonstrate the effectiveness of ATRCM, followed with system security analysis.

A major challenging problem for cloud providers is designing efficient mechanisms for virtual machine (VM) provisioning and allocation. Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. Recently, cloud providers have introduced auction-based models for VM provisioning and allocation which allow users to submit bids for their requested VMs. We formulate the dynamic VM provisioning and allocation problem for the auction-based model as an integer program considering multiple types of resources. We then design truthful greedy and optimal mechanisms for the problem such that the cloud provider provisions VMs based on the requests of the winning users and determines their payments. We show that the proposed mechanisms are truthful, that is, the users do not have incentives to manipulate the system by lying about their requested bundles of VM instances and their valuations. We perform extensive experiments using real workload traces in order to investigate the performance of the proposed mechanisms. Our proposed mechanisms achieve promising results in terms of revenue for the cloud provider.

 

Outsourcing data to a third-party administrative control, as is done in cloud computing, gives rise to security concerns. The data compromise may occur due to attacks by other users and nodes within the cloud. Therefore, high security measures are required to protect data within the cloud. However, the employed security strategy must also take into account the optimization of the data retrieval time. In this paper, we propose Division and Replication of Data in the Cloud for Optimal Performance and Security (DROPS) that collectively approaches the security and performance issues. In the DROPS methodology, we divide a file into fragments, and replicate the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data file that ensures that even in case of a successful attack, no meaningful information is revealed to the attacker. Moreover, the nodes storing the fragments, are separated with certain distance by means of graph T-coloring to prohibit an attacker of guessing the locations of the fragments. Furthermore, the DROPS methodology does not rely on the traditional

cryptographic techniques for the data security; thereby relieving the system of computationally expensive methodologies. We show that the probability to locate and compromise all of the nodes storing the fragments of a single file is extremely low. We also compare the performance of the DROPS methodology with ten other schemes. The higher level of security with slight performance overhead was observed.

Storage-as-a-service offered by cloud service providers (CSPs) is a paid facility that enables organizations to outsource their sensitive data to be stored on remote servers. In this paper, we propose a cloud-based storage scheme that allows the data owner to benefit from the facilities offered by the CSP and enables indirect mutual trust between them. The proposed scheme has four important features: 1) it allows the owner to outsource sensitive data to a CSP, and perform full block-level dynamic operations on the outsourced data, i.e., block modification, insertion, deletion, and append, 2) it ensures that authorized users (i.e., those who have the right to access the owner’s file) receive the latest version of the utsourced data, 3) it enables indirect mutual trust between the owner and the CSP, and 4) it allows the owner to grant or revoke access to the outsourced data. We discuss the security issues of the proposed scheme. Besides, we justify its performance through theoretical analysis and a prototype implementation on Amazon cloud platform to evaluate storage, communication, and computation overheads.

The rapidly increasing power of personal mobile devices (smartphones, tablets, etc.) is providing much richer contents and social interactions to users on the move. This trend however is throttled by the limited battery lifetime of mobile devices and unstable wireless connectivity, making the highest possible quality of service experienced by mobile users not feasible. The recent cloud computing technology, with its rich resources to compensate for the limitations of mobile devices and connections, can potentially provide an ideal platform to support the desired mobile services. Tough challenges arise on how to effectively exploit cloud resources to facilitate mobile services, especially those with stringent interaction delay requirements. In this paper, we propose the design of a Cloud-based, novel Mobile sOcial tV system (CloudMoV). The system effectively utilizes both PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service) cloud services to offer the living-room experience of video watching to a group of disparate mobile users who can interact socially while sharing the video. To guarantee good streaming quality as experienced by the mobile users with time-varying wireless connectivity, we employ a surrogate for each user in the IaaS cloud for video downloading and social exchanges on behalf of the user. The surrogate performs efficient stream transcoding that matches the current connectivity quality of the mobile user. Given the battery life as a key performance bottleneck, we advocate the use of burst transmission from the surrogates to the mobile users, and carefully decide the burst size which can lead to high energy efficiency and streaming quality. Social interactions among the users, in terms of spontaneous textual exchanges, are effectively achieved by efficient designs of data storage with BigTable and dynamic handling of large volumes of concurrent messages in a typical PaaS cloud. These various designs for flexible transcoding c- pabilities, battery efficiency of mobile devices and spontaneous social interactivity together provide an ideal platform for mobile social TV services. We have implemented CloudMoV on Amazon EC2 and Google App Engine and verified its superior performance based on real-world experiments.

Cloud computing uses internet and remote servers for maintaining data and applications. It offers through internet the dynamic virtualized resources, bandwidth and on-demand software's to consumers and promises the distribution of many economical benefits among its adapters. It helps the consumers to reduce the usage of hardware, software license and system maintenance. Simple Object Access Protocol (SOAP) is the system that allows the communications interaction between different web services. SOAP messages are constructed using either HyperText Transport Protocol (HTTP) and/or Extensible Mark-up Language (XML). The new form of Distributed Denial of Service
(DDoS) attacks that could potentially bring down a cloud web services through the use of HTTP and XML. Cloud computing suffers from major security threat problem by HTTP and XML Denial of Service (DoS) attacks. HX-DoS attack is a combination of HTTP and XML messages that are intentionally sent to flood and destroy the communication channel of the cloud service provider. To address the problem of HX-DoS attacks against cloud web services there is a need to distinguish between the legitimate and illegitimate messages. This can be done by using the rule set based detection, called CLASSIE and modulo marking method is used to avoid the spoofing attack. Reconstruct and Drop method is used to make decision and drop the packets on the victim side. It enables us to improve the reduction of false positive rate and increase the detection and filtering of DDoS attacks.

Nowadays, short message service (SMS) is being used in many daily life applications, including healthcare monitoring, mobile banking, mobile commerce, and so on. But when we send an SMS from one mobile phone to another, the information contained in the SMS transmit as plain text. Sometimes this information may be confidential like account numbers, passwords, license numbers, and so on, and it is a major drawback to send such information through SMS while the traditional SMS service does not provide encryption to the information before its transmission. In this paper, we propose an efficient and secure protocol called EasySMS, which provides end-to-end secure communication through SMS between end users. The working of the protocol is presented by considering two different scenarios. The analysis of the proposed protocol shows that this protocol is able to prevent various attacks, including SMS disclosure, over the air modification, replay attack,man-in-the middle attack, and impersonation attack. The EasySMS protocol generates minimum communication and computation overheads as compared with existing SMSSec and PK-SIM protocols. On an average, the EasySMS protocol reduces 51% and 31% of the bandwidth consumption and reduces 62% and 45% of message exchanged during the authentication process in comparison to SMSSec and PK-SIM protocols respectively. Authors claim that EasySMS is the first protocol completely based on the symmetric key cryptography and retain original architecture of cellular network.

Cloud computing is emerging as a prevalent data interactive paradigm to realize users’ data remotely stored in an online cloud server. Cloud services provide great conveniences for the users to enjoy the on-demand cloud applications without considering the local infrastructure limitations. During the data accessing, different users may be in a collaborative relationship, and thus data sharing becomes significant to achieve productive benefits. The existing security solutions mainly focus on the authentication to realize that a user’s privative data cannot be unauthorized accessed, but neglect a subtle privacy issue during a user challenging the cloud server to request other users for data sharing. The challenged access request itself may reveal the user’s privacy no matter whether or not it can obtain the data access permissions. In this paper, we propose a
shared authority based privacy-preserving authentication protocol (SAPA) to address above privacy issue for cloud storage. In the SAPA, 1) shared access authority is achieved by anonymous access request matching mechanism with security and privacy considerations (e.g., authentication, data anonymity, user privacy, and forward security); 2) attribute based access control is adopted to realize that the user can only access its own data fields; 3) proxy re-encryption is applied by the cloud server to provide data sharing among the multiple users. Meanwhile, universal composability (UC) model is established to prove that the SAPA theoretically has the design correctness. It indicates that the proposed protocol realizing privacy-preserving data access authority sharing, is attractive for multi-user collaborative cloud applications.

In the era of big data, many users and companies start to move their data to cloud storage to simplify data management and reduce data maintenance cost. However, security and privacy issues become major concerns because third-party cloud service providers are not always trusty. Although data contents can be protected by encryption, the access patterns that contain important information are still exposed to clouds or malicious attackers. In this paper, we apply the ORAM algorithm to enable privacy-preserving access to big data that are deployed in distributed file systems built upon hundreds or thousands of servers in a single or multiple geo-distribu ted cloud sites. Since the ORAM algorithm would lead to serious access load unbalance among storage servers, we study a data placement problem to achieve a load balanced storage system with improved availability and responsiveness. Due to the NP-hardness of this problem, we propose a low-complexity algorithm that can deal with large-scale problem size with respect to big data. Extensive simulations are conducted to show that our proposed algorithm finds results close to the optimal solution, and significantly outperforms a random data placement algorithm.

The explosive growth of demands on big data processing imposes a heavy burden on computation, storage, and
communication in data centers, which hence incurs considerable operational expenditure to data center providers. Therefore, cost minimization has become an emergent issue for the upcoming big data era. Different from conventional cloud services, one of the main features of big data services is the tight coupling between data and computation as computation tasks can be conducted only when the corresponding data is available. As a result, three factors, i.e., task assignment, data placement and data movement, deeply influence the operational expenditure of data centers. In this paper, we are motivated to study the cost minimization problem via a joint optimization of these three factors for big data services in geo-distributed data centers. To describe the task completion time with the consideration of both data transmission and computation, we propose a two-dimensional Markov chain and derive the average task completion time in closed-form. Furthermore, we model the problem as a mixed-integer non-linear programming (MINLP) and propose an efficient solution to linearize it. The high efficiency of our proposal is validated by extensive simulation based studies.

The buzz-word big-data refers to the large-scale distributed data processing applications that operate on
exceptionally large amounts of data. Google’s MapReduce and Apache’s Hadoop, its open-source implementation,are the defacto software systems for big-data applications. An observation of the MapReduce framework is that the framework generates a large amount of intermediate data. Such abundant information is thrown away after the tasks finish, because MapReduce is unable to utilize them. In this paper, we propose Dache, a data-aware cache framework for big-data applications. In Dache, tasks submit their intermediate results to the cache manager. A task queries the cache manager before executing the actual computing work. A novel cache description scheme and a cache request and reply protocol are designed. We implement Dache by extending Hadoop. Testbed experiment results demonstrate that Dache significantly improves the completion time of MapReduce jobs.

Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.

The capability of selectively sharing encrypted data with different users via public cloud storage may greatly ease security concerns over inadvertent data leaks in the cloud. A key challenge to designing such encryption schemes lies in the efficient management of encryption keys. The desired flexibility of sharing any group of selected documents with any group of users demands different encryption keys to be used for different documents. However, this also implies the necessity of securely distributing to users a large number of keys for both encryption and search, and those users will have to securely store the received keys, and submit an equally large number of keyword trapdoors to the cloud in order to perform search over the shared data. The implied need for secure communication, storage, and complexity clearly renders the approach impractical. In this paper, we address this practical problem, which is largely neglected in the literature, by proposing the novel concept of keyaggregate searchable encryption (KASE) and instantiating the concept through a concrete KASE scheme, in which a data owner only needs to distribute a single key to a user for sharing a large number of documents, and the user only needs to submit a single trapdoor to the cloud for querying the shared documents. The security analysis and performance evaluation both confirm that our proposed schemes are provably secure and practically efficient.

Spurred by service computing and cloud computing, an increasing number of services are emerging on the Internet. As a result, service-relevant data become too big to be effectively processed by traditional approaches. In view of this challenge, a Clustering-based Collaborative Filtering approach (ClubCF) is proposed in this paper, which aims at recruiting similar services in the same clusters to recommend services collaboratively. Technically, this approach is enacted around two stages. In the first stage, the available services are divided into small-scale clusters, in logic, for further processing. At the second stage, a collaborative filtering algorithm is imposed on one of the clusters. Since the number of the services in a cluster is much less than the total number of the services available on the web, it is expected to reduce the online execution time of collaborative filtering. At last, several experiments are conducted to verify the availability of the approach, on a real dataset of 6,225 mashup services collected from ProgrammableWeb.

Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, because the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-Based Encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also design policy updating algorithms for different types of access policies. The analysis show that our scheme is correct, complete, secure and efficient.

Identity-Based Encryption (IBE) which simplifies the public key and certificate management at Public Key Infrastructure (PKI) is an important alternative to public key encryption. However, one of the main efficiency drawbacks of IBE is the overhead computation at Private Key Generator (PKG) during user revocation. Efficient revocation has been well studied in traditional PKI setting, but the cumbersome management of certificates is precisely the burden that IBE strives to alleviate. In this paper, aiming at tackling the critical issue of identity revocation, we introduce outsourcing computation into IBE for the first time and propose a revocable IBE scheme in the server-aided setting. Our scheme offloads most of the key generation related operations during key-issuing and key-update processes to a Key Update Cloud Service Provider, leaving only a constant number of simple operations for PKG and users to perform locally. This goal is achieved by utilizing a novel collusion-resistant technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound the identity component and the time component. Furthermore, we propose another construction which is provable secure under the recently formulized Refereed Delegation of Computation model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

The increasing spread of mobile cloud computing paradigm is changing the traditional mobile communication infrastructure. Today, smartphones can rely on virtual (software) “clones” in the cloud, offering backup/recovery solutions as well as the possibility to offload computations. As a result, clones increase the communication and computation capabilities of smartphones, making their limited batteries last longer. Unfortunately, mobile cloud introduces new privacy risks, since personal information of the communicating
users is distributed among several parties (e.g., cellular network operator, cloud provider). In this paper, we propose a solution implementing an end-to-end anonymous communication protocol between two users in the network, which leverages properties of social networks and ad hoc wireless networks. We consider an adversary model where each party observing a portion of the communication possibly colludes with others to uncover the identity of communicating users. We then extensively analyse the security of our protocol and the anonymity preserved against the above adversaries. Most importantly, we assess the performance of our solution by comparing it to Tor on a real tested of 36 smartphones and relative clones running on Amazon EC2 platform.

The advent of the cloud computing makes storage outsourcing become a rising trend, which promotes the secure remote data auditing a hot topic that appeared in the research literature. Recently some research consider the problemof secure and efficient public data integrity auditing for shared dynamic data. However, these schemes are still not secure against the collusion of cloud storage server and revoked group users during user revocation in practical cloud storage system. In this paper, we figure out the collusion attack in the exiting scheme and provide an efficient public integrity auditing scheme with secure group user revocation based on vector commitment and verifier-local revocation group signature. We design a concrete scheme based on the our scheme definition. Our scheme supports the public checking and efficient user revocation and also some nice properties, such as confidently, efficiency, countability and traceability of secure group user revocation. Finally, the security and experimental analysis show that, compared with its relevant schemes our scheme is also secure and efficient.

Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called ‘auditing-as-a-service’ at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarse-grained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block,which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme,we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions.

Big data, because it can mine new knowledge for economic growth and technical innovation, has recently received considerable attention, and many research efforts have been directed to big data processing due to its high volume, velocity, and variety (referred to as “3V”) challenges. However, in addition to the 3V challenges,
the flourishing of big data also hinges on fully understanding and managing newly arising security and privacy challenges. If data are not authentic, new mined knowledge will be unconvincing; while if privacy is not well addressed, people may be reluctant to share their data. Because security has been investigated as a new dimension, “veracity,” in big data, in this article, we aim to exploit new challenges of big data in terms of privacy, and devote our attention toward efficient and privacy-preserving computing in the big data era. Specifically, we first formalize the general architecture of big data analytics, identify the corresponding privacy requirements, and introduce an efficient and privacy-preserving cosine similarity computing protocol as an example in response to data mining’s efficiency and privacy requirements in the big data era.

A framework for maintaining security & preserving privacy for analysis of sensor data from smart homes, without compromising on data utility is presented. Storing the personally identifiable data as hashed values withholds identifiable information from any computing nodes. However the very nature of smart home data analytics is establishing preventive care. Data processing results should be identifiable to certain users responsible for direct care. Through a separate encrypted identifier dictionary with hashed and actual values of all unique sets of identifiers, we suggest re-identification of any data processing results. However the level of re-identification needs to be controlled, depending on the type of user accessing the results. Generalization and suppression on identifiers from the identifier dictionary before re-introduction could achieve different levels of privacy preservation. In this paper we propose an approach to achieve data security & privacy through out the complete data lifecycle:data generation/collection, transfer, storage, processing and sharing.

Applications Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficien-cy problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appro-priate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environ-ment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly im-proves the accuracy and scalability of service recommender systems over existing approaches.

Recent years have witnessed the trend of leveraging cloud-based services for large scale content storage,
processing, and distribution. Security and privacy are among top concerns for the public cloud environments. Towards these security challenges, we propose and implement, on OpenStack Swift, a new client-side deduplication scheme for securely storing and sharing outsourced data via the public cloud. The originality of our proposal is twofold. First, it ensures better confidentiality towards unauthorized users. That is, every client computes a per data key to encrypt the data that he intends to store in the cloud. As such, the data access is managed by the data owner. Second, by integrating access rights in metadata file, an authorized user can decipher an encrypted file only with his private key.

Cloud computing provides computing resources on demand. It is a promising solution for utility computing.
Increasing number of cloud service providers having similar functionality poses a problem to cloud users of its selection. To assist the users, for selection of a best service provider as per user’s requirement, it is necessary to create a solution. User may provide its QoS expectation and service providers may also express the offers. Experience of existing users may also be beneficial in selection of best cloud service provider. This paper identifies QoS metrics and defines it in such a way that user and provider both can express their expectation and offers respectively into quantified form. A dynamic and flexible framework using Ranked Voting Method is proposed which takes requirement of user as an input and provides a best provider as output.

Motivated by the privacy issues, curbing the adoption of electronic healthcare systems and the wild success of cloud service models, we propose to build privacy into mobile healthcare systems with the help of the private cloud. Our system offers salient features including efficient key management, privacy-preserving data storage, and retrieval, especially for retrieval at emergencies, and auditability for misusing health data. Specifically, we propose to integrate key management from pseudorandom number generator for unlinkability, a secure indexing method for privacypreserving keyword searchwhich hides both search and access patterns based on redundancy, and integrate the concept of attributebased encryption with threshold signing for providing role-based access control with auditability to prevent potential misbehavior, in both normal and emergency cases.

Precision agriculture is aimed at optimizing farming management and it requires records of agricultural work. Farmers conventionally write records on paper but it is difficult and tedious to check past agricultural-work data and control the cost of agricultural products. A system of cultivation management, iFarm, is proposed, which was developed to support efficient farming management. The system consists of smartphone applications, Web browsers and a cloud server. Farmers on farmland can easily refer to work plans, enter field data into the cloud system, and share them with head office in real time by using smartphones. Farmers at head office can analyze data in the cloud system with a Web browser and estimate farming costs and form work plans based on their analyses

The advent of the cloud computing makes storage outsourcing become a rising trend, which promotes the secure remote data auditing a hot topic that appeared in the research literature. Recently some research consider the problemof secure and efficient public data integrity auditing for shared dynamic data. However, these schemes are still not secure against the collusion of cloud storage server and revoked group users during user revocation in practical cloud storage system. In this paper, we figure out the collusion attack in the exiting scheme and provide an efficient public integrity auditing scheme with secure group user revocation based on vector commitment and verifier-local revocation group signature. We design a concrete scheme based on the our scheme definition. Our scheme supports the public checking and efficient user revocation and also some nice properties, such as confidently, efficiency, countability and traceability of secure group user revocation. Finally, the security and experimental analysis show that, compared with its relevant schemes our scheme is also secure and efficient.

With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching,” i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication.

With cloud data services, it is commonplace for data to be not only stored in the cloud, but also shared across multiple users. Unfortunately, the integrity of cloud data is subject to skepticism due to the existence of hardware/software failures and human errors. Several mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. However, public auditing on the integrity of shared data with these existing mechanisms will inevitably reveal confidential information—identity privacy—to public verifiers. In this paper, we propose a novel privacy-preserving mechanism that supports public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute verification metadata needed to audit the correctness of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from public verifiers, who are able to efficiently verify shared data integrity without retrieving the entire file. In addition, our mechanism is able to perform multiple auditing tasks simultaneously instead of verifying them one by one. Our experimental results demonstrate the effectiveness and efficiency of our mechanism when auditing shared data integrity.

Distributed file systems are key building blocks for cloud computing applications based on the MapReduce programming paradigm. In such file systems, nodes simultaneously serve computing and storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that MapReduce tasks can be performed in parallel over the nodes. However, in a cloud computing environment, failure is the norm, and nodes may be upgraded, replaced, and added in the system. Files can also be dynamically created, deleted, and appended. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed as uniformly as possible among the nodes. Emerging distributed file systems in production systems strongly depend on a central node for chunk reallocation. This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size, and may thus become the performance bottleneck and the single point of failure. In this paper, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem. Our algorithm is compared against a centralized approach in a production system and a competing distributed solution presented in the literature. The simulation results indicate that our proposal is comparable with the existing centralized approach and considerably outperforms the prior distributed algorithm in terms of load imbalance factor, movement cost, and algorithmic overhead. The performance of our proposal implemented in the Hadoop distributed file system is further investigated in a cluster environment.

In cloud computing, data generated in electronic form are large in amount. To maintain this data efficiently, there is a necessity of data recovery services. To cater this, in this paper we propose a smart remote data backup algorithm, Seed Block Algorithm (SBA). The objective of proposed algorithm is twofold; first it help the users to collect information from any remote location in the absence of network connectivity and second to recover the files in case of the file deletion or if the cloud gets destroyed due to any reason. The time related issues are also being solved by proposed SBA such that it will take minimum time for the recovery process. Proposed SBA also focuses on the security concept for the back-up files stored at remote server, without using any of the existing encryption techniques.

With the character of low maintenance, cloud computing provides an economical and efficient solution for sharing group resource among cloud users. Unfortunately, sharing data in a multi-owner manner while preserving data and identity privacy from an untrusted cloud is still a challenging issue, due to the frequent change of the membership. In this paper, we propose a secure multi-owner data sharing scheme, named Mona, for dynamic groups in the cloud. By leveraging group signature and dynamic broadcast encryption techniques, any cloud user can anonymously share data with others. Meanwhile, the storage overhead and encryption computation cost of our scheme are independent with the number of revoked users. In addition, we analyze the security of our scheme with rigorous proofs, and demonstrate the efficiency of our scheme in experiments.

In this paper, wehave propose a user authentication scheme for cloud computing. The proposed framework providesmutual authentication and session key agreement in cloud computing environment. The scheme executesin three phases such as server initialization phase, registration phase, authentication phase. Detailed security analyses have been made to validate the efficiency of the scheme. Further, the scheme has the resistance to possible attacks in cloud computing.

Media streaming applications have recently attracted a large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is economically inefficient to provide streaming distribution with guaranteed QoS relying only on central resources at a media content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., Video on Demand (VoD) providers) can use to obtain streaming resources that match the demand. Media content providers are charged for the amount of resources allocated (reserved) in the cloud. Most of the existing cloud providers employ a pricing model for the reserved resources that is based on non-linear time-discount tariffs (e.g., Amazon CloudFront and Amazon EC2). Such a pricing scheme offers discount rates depending non-linearly on the period of time during which the resources are reserved in the cloud. In this case, an open problem is to decide on both the right amount of resources reserved in the cloud, and their reservation time such that the financial cost on the media content provider is minimized.We propose a simple - easy to implement - algorithm for resource reservation that maximally exploits discounted rates offered in the tariffs, while ensuring that sufficient resources are reserved in the cloud. Based on the prediction of demand for streaming capacity, our algorithm is carefully designed to reduce the risk of making wrong resource allocation decisions. The results of our numerical evaluations and simulations show that the proposed algorithm significantly reduces the monetary cost of resource allocations in the cloud as compared to other conventional schemes.

Virtualized cloud-based services can take advantage of statistical multiplexing across applications to yield significant cost savings. However, achieving similar savings with real-time services can be a challenge. In this paper, we seek to lower a provider's costs for real-time IPTV services through a virtualized IPTV architecture and through intelligent time-shifting of selected services. Using Live TV and Video-on-Demand (VoD) as examples, we show that we can take advantage of the different deadlines associated with each service to effectively multiplex these services. We provide a generalized framework for computing the amount of resources needed to support multiple services, without missing the deadline for any service. We construct the problem as an optimization formulation that uses a generic cost function. We consider multiple forms for the cost function (e.g., maximum, convex and concave functions) reflecting the cost of providing the service. The solution to this formulation gives the number of servers needed at different time instants to support these services. We implement a simple mechanism for time-shifting scheduled jobs in a simulator and study the reduction in server load using real traces from an operational IPTV network. Our results show that we are able to reduce the load by ~ 24% (compared to a possible ~ 31%). We also show that there are interesting open problems in designing mechanisms that allow time-shifting of load in such environments.

To provide fault tolerance for cloud storage, recent studies propose to stripe data across multiple cloud vendors. However, if a cloud suffers from a permanent failure and loses all its data, we need to repair the lost data with the help of the other surviving clouds to preserve data redundancy. We present a proxy-based storage system for fault-tolerant multiple-cloud storage called NCCloud, which achieves cost-effective repair for a permanent single-cloud failure. NCCloud is built on top of a network-coding-based storage scheme called the functional minimum-storage regenerating (FMSR) codes, which maintain the same fault tolerance and data redundancy as in traditional erasure codes (e.g., RAID-6), but use less repair traffic and, hence, incur less monetary cost due to data transfer. One key design feature of our FMSR codes is that we relax the encoding requirement of storage nodes during repair, while preserving the benefits of network coding in repair. We implement a proof-of-concept prototype of NCCloud and deploy it atop both local and commercial clouds. We validate that FMSR codes provide significant monetary cost savings in repair over RAID-6 codes, while having comparable response time performance in normal cloud storage operations such as upload/download.

Cloud computing has emerged as one of the most influential paradigms in the IT industry in recent years. Since this new computing technology requires users to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing; however, most of them suffer from inflexibility in implementing complex access control policies. In order to realize scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in this paper, we propose hierarchical attribute-set-based encryption (HASBE) by extending ciphertext-policy attribute-set-based encryption (ASBE) with a hierarchical structure of users. The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits flexibility and fine-grained access control in supporting compound attributes of ASBE. In addition, HASBE employs multiple value assignments for access expiration time to deal with user revocation more efficiently than existing schemes. We formally prove the security of HASBE based on security of the ciphertext-policy attribute-based encryption (CP-ABE) scheme by Bethencourt and analyze its performance and computational complexity. We implement our scheme and show that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing with comprehensive experiments.

Secure data transmission is a critical issue for wireless sensor networks (WSNs). Clustering is an effective and practical way
to enhance the system performance of WSNs. In this paper, we study a secure data transmission for cluster-based WSNs (CWSNs), where the clusters are formed dynamically and periodically. We propose two secure and efficient data transmission (SET) protocols for CWSNs, called SET-IBS and SET-IBOOS, by using the identity-based digital signature (IBS) scheme and the identity-based online/ offline digital signature (IBOOS) scheme, respectively. In SET-IBS, security relies on the hardness of the Diffie-Hellman problem in the pairing domain. SET-IBOOS further reduces the computational overhead for protocol security, which is crucial for WSNs, while its security relies on the hardness of the discrete logarithm problem. We show the feasibility of the SET-IBS and SET-IBOOS protocols with respect to the security requirements and security analysis against various attacks. The calculations and simulations are provided to illustrate the efficiency of the proposed protocols. The results show that the proposed protocols have better performance than the existing secure protocols for CWSNs, in terms of security overhead and energy consumption.

Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.

Cloud computing is a new approach in the field of information technology and development of computer technologies based on the World Wide Web. One of the most important challenges in this area is the security of cloud computing. On the other hand the security of access to critical and confidential information in banks, institutions and etc is extremely essential. Sometimes even with the enormous costs, it is not fully guaranteed and it is compromised by the attackers. In this paper by providing a novel method, we improve the security of data access in cloud computing for a company or any other specific locations using the location-based encryption.

Cloud computing is an Internet-based computing. Computing services, such as data, storage, software, computing,and application, are delivered to local devices through Internet. The major security issue of cloud computing is that the cloud provider must ensure that their infrastructure is secure, and that prevent illegal data accesses from outsiders, other clients, or even the unauthorized cloud employees. In this paper, we deal with cloud security services including key agreement and authentication. By using Elliptic Curve Diffie-Hellman (ECDH) and symmetric bivariate polynomial based secret sharing, we design the secure cloud computing (SCC). Two types of SCC are proposed. One requires a trusted third party (TTP), and the other does not need a TTP. Also, our SCC can be extended to multi-server SCC (MSCC) to fit an environment, where each multi-server system contains multiple servers to collaborate for serving applications. Due to the strong security and operation efficiency, the proposed SCC and MSCC are extremely suitable for use in cloud computing.

Cloud computing environments provide on-demand resource provisioning, allowing applications to elastically scale. However, application benchmarks currently being used to test cloud management systems are not designed for this purpose. This results in resource underprovisioning and quality-of-service (QoS) violations when systems tested using these benchmarks are deployed in production environments. We present C-MART, a benchmark designed to emulate a modern web application running in a cloud computing environment. It is designed using the cloud computing paradigm of elastic scalability at every application tier and utilizes modern web-based technologies such as HTML5, AJAX, jQuery, and SQLite. C-MART consists of a web application, client emulator, deployment server, and scaling API. The deployment server automatically deploys and configures the test environment in orders of magnitude less time than current benchmarks. The scaling API allows users to define and provision their own customized datacenter. The client emulator generates the web workload for the application by emulating complex and varied client behaviors, including decisions based on page content and prior history. We show that C-MART can detect problems in management systems that previous benchmarks fail to identify, such as an increase from 4.4 to 50 percent error in predicting server CPU utilization and resource underprovisioning in 22 percent of QoS measurements.

Health is the greatest invention of technology in medicine. Earlier slow and erroneous processes are replaced by precise and faultless methods involving fast internet services. These techniques allow real-time data accessibility with proper authentication. The idea is based on cloud-computing and real time streaming of videos. The information is made available on the WEB in a suitable format, from where, it can be accessed by authorized medical staff. Cloud computing has a revolutionary effect on telemedicine. Many medical professionals are already using advance telehealth application of cloud computing. According to various specialists and researchers, cloud computing can improve healthcare services to an undoubtedly large extent. This paper discusses the advancement in utilization of cloud computing in field of telehealth. It can contribute to improve health scenario all over the world.

This paper presents a new scheduling approach to focus on providing a solution for online scheduling problem of real-time tasks using “Infrastructure as a Service” model offered by cloud computing. The real time tasks are scheduled pre-emptively with the intent of maximizing the total utility and efficiency. In traditional approach, the task is scheduled non- pre-emptively with two different types of Time Utility Functions (TUFs) - a profit time utility function and a penalty time utility function. The task with highest expected gain is executed. When a new task arrives with highest priority then it cannot be taken for execution until it completes the currently running task. Therefore the higher priority task is waiting for a longer time. This scheduling method sensibly aborts the task when it misses its deadline. Note that, before a task is aborted, it consumes system resources including network bandwidth, storage space and processing power. This leads to affect the overall system performance and response time of a task. In our approach, a preemptive online scheduling with task migration algorithm for cloud computing environment is proposed in order to minimize the response time and to improve the efficiency of the tasks. Whenever a task misses its deadline, it will be migrated the task to another virtual machine. This improves the overall system performance and maximizes the total utility. Our simulation results outperform the traditional scheduling algorithms such as the Earliest Deadline First (EDF) and an earlier scheduling approach based on the similar model.

Using Cloud Storage, users can remotely store their data and enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources, without the burden of local data storage and maintenance.However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in Cloud Computing a formidable task, especially for users with constrained computing resources. Moreover, users should be able to just use the cloud storage as if it is local, without worrying about the need to verify its integrity. Thus, enabling public auditability for cloud storage is of critical importance so that users can resort to a third party auditor (TPA) to check the integrity of outsourced data and be worry-free. To securely introduce an effective TPA, the auditing process should bring in no new vulnerabilities towards user data privacy, and introduce no additional online burden to user. In this paper, we propose a secure cloud storage system supporting privacy-preserving public auditing. We further extend our result to enable the TPA to perform audits for multiple users simultaneously and efficiently. Extensive security and performance analysis show the proposed schemes are provably secure and highly efficient.

Cloud computing has emerging as a promising pattern for data outsourcing and high-quality data services. However, concerns of sensitive information on cloud potentially causes privacy problems. Data encryption protects data security to some extent, but at the cost of compromised efficiency. Searchable symmetric encryption (SSE) allows retrieval of encrypted data over cloud. In this paper, we focus on addressing data privacy issues using SSE. For the first time, we formulate the privacy issue from the aspect of similarity relevance and scheme robustness. We observe that server-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. To eliminate the leakage, we propose a two-round searchable encryption (TRSE) scheme that supports top-$(k)$ multikeyword retrieval. In TRSE, we employ a vector space model and homomorphic encryption. The vector space model helps to provide sufficient search accuracy, and the homomorphic encryption enables users to involve in the ranking while the majority of computing work is done on the server side by operations only on ciphertext. As a result, information leakage can be eliminated and data security is ensured. Thorough security and performance analysis show that the proposed scheme guarantees high security and practical efficiency.

Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile communications and cloud computing technologies to provide feedback decision support, has been considered as a revolutionary approach to improving the quality of healthcare service while lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients' privacy and intellectual property of monitoring service providers, which could deter the wide adoption of mHealth technology. This paper is to address this important problem and design a cloud-assisted privacy preserving mobile health monitoring system to protect the privacy of the involved parties and their data. Moreover, the outsourcing decryption technique and a newly proposed key private proxy reencryption are adapted to shift the computational complexity of the involved parties to the cloud without compromising clients' privacy and service providers' intellectual property. Finally, our security and performance analysis demonstrates the effectiveness of our proposed design.

This paper presents result analysis of K-Mediod algorithm, implemented on Hadoop Cluster by using Map-Reduce concept. Map-Reduce are programming models which authorize the managing of huge datasets in parallel, on a large number of devices. It is especially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce is supposed to be framework of “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e., make span) of a set of MapReduce duty. For various applications like word count, grep, terasort and parallel K-Mediod Clustering Algorithm, it has been observed that as the number of node increases, execution time decreases. In this paper we verified Map Reduce applications and found as the amount of nodes increases the completion time decreases.

We present Symbolic Quick Error Detection (Symbolic QED), a structured approach for logic bug detection and localization which can be used both during pre-silicon design verification as well as post-silicon validation and debug. This new methodology leverages prior work on Quick Error Detection (QED) which has been demonstrated to drastically reduce the latency, in terms of the number of clock cycles, of error detection following the activation of a logic (or electrical) bug. QED works through software transformations, including redundant execution and control flow checking, of the applied tests. Symbolic QED combines these error detecting QED transformations with bounded model checking-based formal analysis to generate minimal-length bug activation traces that detect and localize any logic bugs in the design. We demonstrate the practicality and effectiveness of Symbolic QED using the Open SPARC T2, a 500-million-transistor open-source multicore System-on-Chip (SoC) design, and using "difficult" logic bug scenarios observed in various state-of-the-art commercial multicore SoCs. Our results show that Symbolic QED: (i) is fully automatic, unlike manual techniques in use today that can be extremely time-consuming and expensive; (ii) requires only a few hours in contrast to manual approaches that might take days (or even months) or formal techniques that often take days or fail completely for large designs; and (iii) generates counter-examples (for activating and detecting logic bugs) that are up to 6 orders of magnitude shorter than those produced by traditional techniques. Significantly, this new approach does not require any additional hardware.

Security and privacy are the major concerns in cloud computing as users have limited access on the stored data at the remote locations managed by different service providers.These become more challenging especially for the data generated from the wearable devices as it is highly sensitive and heterogeneous in nature. Most of the existing techniques reported in the literature are having high computation and communication costs and are vulnerable to various known attacks, which reduce their importance for applicability in real-world environment. Hence, in this paper, we propose a new cloud based user authentication scheme for secure authentication of medical data. After successful mutual authentication between a user and wearable sensor node, both establish a secret session key that is used for future secure communications. The extensively-used Real-Or-Random (ROR) model based formal security analysis and the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool based formal security verification show that the proposed scheme provides the session-key security and protects active attacks. The proposed scheme is also informally analyzed to show its resilience against other known attacks. Moreover, we have done a detailed comparative analysis for the communication and computation costs along with security and functionality features which proves its efficiency in comparison to the other existing schemes of its category

With the emergence of new technologies, new academic trends introduced into Educational system which results in large data which is unregulated and it is also challenge for students to prefer to those academic courses which are helpful in their industrial training and increases their career prospects. Another challenge is to convert the unregulated data into structured and meaningful information there is need of Data Mining Tools. Hadoop Distributed File System is used to hold large amount of data. The Files are stored in a redundant fashion across multiple machines which ensure their endurance to failure and parallel applications. Knowledge extracted using Map Reduce will be helpful in decision making for students to determine courses chosen for industrial trainings. In this paper, we are deriving preferable courses for pursuing training for students based on course combinations. Here, using HDFS, tasks run over Map Reduce and output is obtained after aggregation of results.

Cloud storage auditing schemes for shared data refer to checking the integrity of cloud data shared by a group of users. User revocation is commonly supported in such schemes, as users may be subject to group membership changes for various reasons. Previously, the computational overhead for user revocation in such schemes is linear with the total number of file blocks possessed by a revoked user. The overhead, however, may become a heavy burden because of the sheer amount of the shared cloud data. Thus, how to reduce the computational overhead caused by user revocations becomes a key research challenge for achieving practical cloud data auditing. In this paper, we propose a novel storage auditing scheme that achieves highly-efficient user revocation independent of the total number of file blocks possessed by the revoked user in the cloud. This is achieved by exploring a novel strategy for key generation and a new private key update technique. Using this strategy and the technique, we realize user revocation by just updating the nonrevoked group users’ private keys rather than authenticators of the revoked user. The integrity auditing of the revoked user’s data can still be correctly performed when the authenticators are not updated. Meanwhile, the proposed scheme is based on identity-base cryptography, which eliminates the complicated certificate management in traditional Public Key Infrastructure (PKI) systems. The security and efficiency of the proposed scheme are validated via both analysis and experimental results.

Bigdata is a potential instrument to transform traditional governance into smart governance. There are a long debate and discussion on the application of big data for the transformation of traditional public administration to modern and smart public administration in the academician, researchers, and policymakers. This study aims to explore the suitability and applicability of big data for smart governance of public agencies. A systematic review of literature and meta analysis method is employed with various levels of scales and indicators. Literature survey shows that a number of models have been developed to explain smart governance but systematic research on the suitability and applicability of big data for smart governance of public agencies is still lacking. This article argues that the application of big data for smart governance in the public sector can increase the efficiency of the public agencies fastest public service delivery, enhancing transparency, reducing public hassle and helping to the become a smart agency. This paper further argues that implementation of big data for smart governance has a significant role in timely, error-free, appropriate and cost effective service delivery to citizens which leads to the sustainable economic development of a country. The findings suggest that every public-sector agency should be brought under smart governance which should be a fully promoted under big data technologies for easy access, transparent and accountable, and hassle-free public agencies.

With the emergence of new technologies, new academic trends introduced into Educational system which results in large data which is unregulated and it is also challenge for students to prefer to those academic courses which are helpful in their industrial training and increases their career prospects. Another challenge is to convert the unregulated data into structured and meaningful information there is need of Data Mining Tools. Hadoop Distributed File System is used to hold large amount of data. The Files are stored in a redundant fashion across multiple machines which ensure their endurance to failure and parallel applications. Knowledge extracted using Map Reduce will be helpful in decision making for students to determine courses chosen for industrial trainings. In this paper, we are deriving preferable courses for pursuing training for students based on course combinations. Here, using HDFS, tasks run over Map Reduce and output is obtained after aggregation of results.

This paper presents result analysis of K-Mediod algorithm, implemented on Hadoop Cluster by using Map-Reduce concept. Map-Reduce are programming models which authorize the managing of huge datasets in parallel, on a large number of devices. It is especially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce is supposed to be framework of “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e., make span) of a set of MapReduce duty. For various applications like word count, grep, terasort and parallel K-Mediod Clustering Algorithm, it has been observed that as the number of node increases, execution time decreases. In this paper we verified Map Reduce applications and found as the amount of nodes increases the completion time decreases.

We present Symbolic Quick Error Detection (Symbolic QED), a structured approach for logic bug detection and localization which can be used both during pre-silicon design verification as well as post-silicon validation and debug. This new methodology leverages prior work on Quick Error Detection (QED) which has been demonstrated to drastically reduce the latency, in terms of the number of clock cycles, of error detection following the activation of a logic (or electrical) bug. QED works through software transformations, including redundant execution and control flow checking, of the applied tests. Symbolic QED combines these errordetecting QED transformations with bounded model checking-based formal analysis to generate minimal-length bug activation traces that detect and localize any logic bugs in the design. We demonstrate the practicality and effectiveness of Symbolic QED using the OpenSPARC T2, a 500-million-transistor open-source multicore System-on-Chip (SoC) design, and using "difficult" logic bug scenarios observed in various state-of-the-art commercial multicore SoCs. Our results show that Symbolic QED: (i) is fully automatic, unlike manual techniques in use today that can be extremely time-consuming and expensive; (ii) requires only a few hours in contrast to manual approaches that might take days (or even months) or formal techniques that often take days or fail completely for large designs; and (iii) generates counter-examples (for activating and detecting logic bugs) that are up to 6 orders of magnitude shorter than those produced by traditional techniques. Significantly, this new approach does not require any additional hardware.

Cloud storage auditing schemes for shared data refer to checking the integrity of cloud data shared by a group of users. User revocation is commonly supported in such schemes, as users may be subject to group membership changes for various reasons. Previously, the computational overhead for user revocation in such schemes is linear with the total number of file blocks possessed by a revoked user. The overhead, however, may become a heavy burden because of the sheer amount of the shared cloud data. Thus, how to reduce the computational overhead caused by user revocations becomes a key research challenge for achieving practical cloud data auditing. In this paper, we propose a novel storage auditing scheme that achieves highly-efficient user revocation independent of the total number of file blocks possessed by the revoked user in the cloud. This is achieved by exploring a novel strategy for key generation and a new private key update technique. Using this strategy and the technique, we realize user revocation by just updating the non revoked group users’ private keys rather than authenticators of the revoked user. The integrity auditing of the revoked user’s data can still be correctly performed when the authenticators are not updated. Meanwhile, the proposed scheme is based on identity-base cryptography, which eliminates the complicated certificate management in traditional Public Key Infrastructure (PKI) systems. The security and efficiency of the proposed scheme are validated via both analysis and experimental results.

With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngers. However, for many users, they spend a lot of time to get their favorite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Therefore, this paper proposes a micro-video recommendation system. The recommendation algorithms are the core of this system. Traditional recommendation algorithms include content-based recommendation, collaboration recommendation algorithms, and so on. At the Bid Data times, the challenges what we meet are data scale, performance of computing, and other aspects. Thus, this paper improves the traditional recommendation algorithms, using the popular parallel computing framework to process the Big Data. Slope one recommendation algorithm is a parallel computing algorithm based on MapReduce and Hadoop framework which is a high performance parallel computing platform. The other aspect of this system is data visualization. Only an intuitive, accurate visualization interface, the viewers and producers can find what they need through the micro-video recommendation system.

System Architecture

Project Overview
Fetching the users and associated interests youtube videos and recommends the nearest matching video(singer based) with neural network algorithm. Video recommendation with accuracy.

System requirement
Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse

Software Requirement Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings, Core java.
IDE : Netbeans.

The main objective of the work presented within this paper was to design and implement the system for twitter data analysis and visualization in R environment using the big data processing technologies. Our focus was to leverage existing big data processing frameworks with its storage and computational capabilities to support the analytical functions implemented in R language. We decided to build the backend on top of the Apache Hadoop framework including the Hadoop HDFS as a distributed filesystem and MapReduce as a distributed computation paradigm. RHadoop packages were then used to connect the R environment to the processing layer and to design and implement the analytical functions in a distributed manner. Visualizations were implemented on top of the solution as a RShiny application.

Enterprise users at different geographic locations generate large-volume data and store their data at different geographic datacenters. These users may also issue ad hoc queries of big data analytics on the  stored data to identify valuable information in order to help them make strategic decisions. However, it is well known that querying such large-volume big data usually is time-consuming and costly. Sometimes, users are only interested in timely approximate rather than exact query results. When this approximation is the case, applications must sacrifice either timeliness or accuracy by allowing either the latency of delivering more accurate results or the accuracy error of delivered results based on the samples of the data, rather than the entire set of data itself. In this paper, we study the QoSaware data replications and placements for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on placing the samples of the source data with certain error bounds at some strategic datacenters to meet users’ stringent query response time. We propose an efficient algorithm for evaluating a set of big data analytic queries with the aim to minimize the evaluation cost of the queries while meeting their response time requirements. We demonstrate the effectiveness of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.

 

Big data analysis is usually casted into parallel jobs running on geo-distributed data centers. Different from a single data center, geo-distributed environment imposes big challenges for big data analytics due to the limited network bandwidth between data centers located in different regions.Although research efforts have been devoted to geo-distributed big data, the results are still far from being efficient because of their suboptimal performance or high complexity. In this paper, we propose a traffic-aware task placement to minimize job completion time of big data jobs. We formulate the problem as a non-convex optimization problem and design an algorithm to solve it with proved performance gap. Finally, extensive simulations are conducted to evaluate the performance of our proposal. The simulation results show that our algorithm can reduce job completion time by 40%, compared to a conventional approach that aggregates all data for centralized processing. Meanwhile, it has only 10% performance gap with the optimal solution, but its problem-solving time is extremely small.

Given a set of files that show a certain degree of similarity, we consider a novel problem of performing data redundancy elimination across a set of distributed worker nodes in a shared-nothing in-memory big data analytic system. The redundancy elimination scheme is designed in a manner that is: (i) space-efficient: the total space needed to store the files is minimized and, (ii) access-isolation: data shuffling among server is  also minimized. In this paper, we first show that finding an access-efficient and space optimal solution is an NP-Hard problem. Following this, we present the file partitioning algorithms that locate access-efficient solutions in an incremental manner with minimal algorithm time complexity (polynomial time). Our experimental verification on multiple data sets confirms that the proposed file partitioning solution is able to achieve compression ratio close to the optimal compression performance achieved by a centralized solution.

 

Even though connectivity services have been introduced in many of the most recent car models, access to vehicle data is currently limited due to its proprietary nature. The European project AutoMat has therefore developed an open Marketplace providing a single point of access for brandindependent vehicle data. Thereby, vehicle sensor data can be leveraged for the design and implementation of entirely new services even beyond traffic-related applications (such as hyperlocal  traffic forecasts). This paper presents the architecture for a Vehicle Big Data Marketplace as enabler of cross-sectorial and innovative vehicle data services. Therefore, the novel Common Vehicle Information Model (CVIM) is defined as an open and  harmonized data model, allowing the aggregation of brandindependent and generic data sets. Within this work the  realization of a prototype CVIM and Marketplace implementation is presented. The two use-cases of local weather prediction and road quality measurements are introduced to show the applicability of the AutoMat concept and prototype to nonautomotive applications.

Healthcare has increased its overall value by  adopting big data methods to analyze and understand its data from various sources. This article presents big data from the perspective of improving healthcare services and, also, offers a holistic view of system security and factors determining security breaches.

Message importance measure (MIM) is applicable to characterize the importance of information in the scenario of big data, similar to entropy in information theory. In fact, MIM with a variable parameter can make an effect on the characterization of distribution. Furthermore, by choosing an appropriate parameter of MIM, it is possible to emphasize the message importance of a certain probability element in a distribution. Therefore, parametric MIM can play a vital role in anomaly detection of big data by focusing on probability of an anomalous event. In this paper, we propose a parameter selection method of MIM focusing on a probability element  and then present its major properties. In addition, we discuss the parameter selection with prior probability, and investigate the availability in a statistical processing model of big data for anomaly detection problem.

In big data era, data are usually stored in databases for easy access and utilization, which are now woven into every aspect of our lives. However, traditional relational databases cannot address users’ demands for quick data access and calculating, since they cannot process data in a distributed way. To tackle this problem, non-relational databases such as MongoDB have emerged up and been applied in various Scenarios. Nevertheless, it should be noted that most MongoDB products fail to consider user’s data privacy. In this paper, we propose a practical encrypted MongoDB ( i.e., CryptMDB ). Specifically, we utilize an additive homomorphic asymmetric cryptosystem to encrypt user’s data and achieve strong privacy protection. Security analysis indicates that the CryptMDB can achieve confidentiality of user’s data and prevent adversaries from illegally gaining access to the database. Furthermore, extensive experiments demonstrate that the CryptMDB achieves better efficiency than existing relational database in terms of data access and calculating.

As accessing computing resources from the remote cloud for big data processing inherently incurs high end-toend (E2E) delay for mobile users, cloudlets, which are deployed at the edge of networks, can potentially mitigate this problem. Although load offloading in cloudlet networks has been proposed, placing the cloudlets to minimize the deployment cost of cloudlet providers and E2E delay of user requests has not been addressed so far. The locations and number of cloudlets and their servers  have a crucial impact on both the deployment cost and E2E delay of user requests. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing strategy (CAPABLE) to optimize the tradeoff between the deployment cost and E2E delay. When cloudlets are already placed in the network, we also design a load allocation scheme to minimize the E2E delay of user requests by assigning the workload of each region to the suitable cloudlets. The performance of CAPABLE is demonstrated by extensive simulation results.

The increased density of base stations (BSs) may significantly add complexity to network management mechanisms and hamper them from efficiently managing the network. In this paper, we propose a big-data-driven network partitioning and optimization framework to reduce the complexity of the networking mechanisms. The proposed framework divides the entire radio access network (RAN) into multiple sub-RANs and each sub-RAN can be managed independently. Therefore, the complexity of the network management can be reduced.

Quantifying the relationships among BSs is challenging in the network partitioning. We propose to extract three networking features from mobile traffic data to discover the relationships. Based on these features, we engineer the network partitioning solution in three steps. First, we design a hierarchical clustering analysis (HCA) algorithm to divide the entire RAN into sub- RANs. Second, we implement a traffic load balancing algorithm to characterize the performance of the network partitioning. Third, we adapt the weights of networking features in the HCA algorithm to optimize the network partitioning. We validate the proposed solution through simulations designed based on real mobile network traffic data. The simulation results reveal the  impacts of the RAN partitioning on the networking performance and the computational complexity of the networking mechanism.

There is a growing trend towards attacks on database privacy due to great value of privacy information stored in big data set. Public’s privacy are under threats as adversaries are continuously cracking their popular targets such as bank accounts. We find a fact that existing models such as K-anonymity, group records based on quasi-identifiers, which harms the data utility a lot. Motivated by this, we propose a sensitive attribute-based privacy model. Our model is the early work of grouping records based on sensitive attributes instead of quasi-identifiers which is popular in existing models. Random shuffle is used to maximize information entropy inside a group while the marginal distribution maintains the same before and after shuffling, therefore, our method maintains a better data utility than existing models. We have conducted extensive experiments which confirm that our model can achieve a satisfying privacy level without sacrificing data utility while guarantee a higher efficiency.

Thanks to recent advance in massive social data and increasingly mature big data mining technologies, information diffusion and its control strategies have attracted much attention, which play pivotal roles in public opinion control, virus marketing as well as other social applications. In this paper, relying on social big data, we focus on the analysis and control of information diffusion. Specifically, we commence with analyzing the topological role of the social strengths, i.e., tie strength, partial strength, value strength, and their corresponding symmetric as well as asymmetric forms. Then, we define two critical points for the cascade information diffusion model, i.e., the information coverage critical point (CCP) and the information heat critical point (HCP). Furthermore, based on the two real-world datasets, the proposed two critical points are verified and analyzed. Our work may be beneficial in terms of analyzing and designing the information diffusion algorithms and relevant control strategies.

Geosocial Network data can be served as an asset for the authorities to make real-time decisions and future planning by analyzing geosocial media posts. However, there are millions of Geosocial Network users who are producing overwhelming of data, called “Big Data” that is challenging to be analyzed and make real-time decisions. Therefore, in this paper, we proposed an efficient system for exploring Geosocial Networks while harvesting data as well as user’s location information. A system architecture is proposed that processes an abundant amount of various social networks’ data to monitor Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning. The proposed system consists of five layers, i.e., data collection, data processing, application, communication, and data storage. The system deploys Spark at the top of the Hadoop ecosystem in order to run real-time analyses. Twitter and Flickr are analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, fires, Ebola virus, and snow. The system is evaluated with respect to efficiency while considering system throughput. We proved that the system has higher throughput and is capable of analyzing massive Geosocial Network data at real-time.

In the age of big data, a traditional scanning search pattern is gradually becoming unfit for a satisfying user experience due to its lengthy computing process. In this paper, we propose a sampling-based approximate search framework called Hermes, to meet user’s query demand for both accurate and efficient results. A novel metric, (ε, δ)-approximation, is presented to uniformly measure accuracy and efficiency for a big data search service, which enables Hermes to work out a feasible searching job. Based on this, we employ the bootstrapping technique to further speed up the search process. Moreover, an incremental sampling strategy is investigated to process homogeneous queries; in addition, the reuse theory of historical results is also studied for the scenario of appending data. Theoretical analyses and experiments on a real-world dataset demonstrate that Hermes is capable of producing approximate results meeting the preset query requirements with both high accuracy and efficiency.

With the ubiquitous deployment of the mobile devices with increasingly better communication and computation capabilities, an emerging model called spatial crowdsourcing is proposed to solve the problem of unstructured big data by publishing location-based tasks to participating workers. However, massive spatial data generated by spatial crowdsourcing entails a critical challenge that the system has to guarantee quality control of crowdsourcing. This paper first studies a practical problem of task assignment, namely reliability aware spatial crowdsourcing (RA-SC), which takes the constrained tasks and numerous dynamic workers into consideration. Specifically, the worker confidence is introduced to reflect the completion reliability of the assigned task. Our RA-SC problem is to perform task assignments such that the reliability under budget constraints is maximized. Then, we reveal the typical property of the proposed problem, and design an effective strategy to achieve a high reliability of the task assignment. Besides the theoretical analysis, extensive experimental results also demonstrate that the proposed strategy is stable and effective for spatial crowdsourcing.

As more than 2.5 quintillion bytes of data are generated every day, the era of big data is undoubtedly upon us. Running analysis on extensive datasets is a challenge. Fortunately, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference in many cases. Censoring provides us a natural option for data reduction. However, the data chosen by censoring occur nonuniformly, which may not relieve the computational resource requirement. In this paper, we propose a dynamic, queuing method to smooth out the data processing without sacrificing the convergence performance of censoring. The proposed method entails simple, closed-form updates, and has no loss in terms of accuracy comparing to the original adaptive censoring method.Simulation results validate its effectiveness.

Secure data deduplication can significantly reduce the communication and storage overheads in cloud storage services, and has potential applications in our big data-driven society. Existing data deduplication schemes are generally designed to either resist brute-force attacks or ensure the efficiency and data availability, but not both conditions. We are also not aware of any existing scheme that achieves accountability, in the sense of reducing duplicate information disclosure (e.g., to determine whether plaintexts of two encrypted messages are identical). In this paper, we investigate a three-tier cross-domain architecture, and propose an efficient and privacy-preserving big data deduplication in cloud storage (hereafter referred to as EPCDD). EPCDD achieves both privacy-preserving and data availability, and resists brute-force attacks. In addition, we take accountability into consideration to offer better privacy assurances than existing schemes. We then demonstrate that EPCDD outperforms existing competing schemes, in terms of computation, communication and storage overheads. In addition, the time complexity of duplicate search in EPCDD is logarithmic.

Bringing Big data technologies into agriculture presents a significant challenge; at the same time, this technology contributes effectively in many countries’ economic and social development. In this work, we will study environmental data provided by precision agriculture information technologies, which represents a crucial source of data in need of being wisely managed and analyzed with appropriate methods and tools in order to extract the meaningful information. Our main purpose through this paper is to propose an effective Big data architecture based on profiling system which can assist (among others) producers, consulting companies, public bodies and research laboratories to make better decisions by providing them real time data processing, and a dynamic big data service composition method, to enhance and monitor the agricultural productivity. Thus, improve their traditional decision making process, and allow better management of the natural resources.

Success of web 2.0 brings online information overload. An exponential growth of customers, services and online information has been observed in last decade. It yields big data investigation problem for service recommendation system. Traditional recommender systems often put up with scalability, lack of security and efficiency problems. Users preferences are almost ignored. So, the requirement of robust  ecommendation system is enhanced now a days. In this paper, we present review based service recommendation to dynamically recommend services to the users. Keywords are extracted from passive users
reviews and a rating value is given to every new keyword observed in the dataset. Sentiment analysis is performed on these rating values and top-k services recommendation list is provided to users. To make the system more effective and robust hadoop framework is used

Internet of Things adoption across industries has proven to be beneficial in providing business value by transforming the way data is utilized in decision making and visualization. Power industry has for long struggled with traditional ways of operating and has suffered from issues like instability, blackouts,etc. The move towards smart grid has thus received lot of acceptance. This paper presents the Internet of Things deployment in grid, namely WAMS, and the challenges it present in terms of the Big Data it aggregates. Better insight into the problem is provided with the help of Indian Grid case studies.

Road and traffic accidents are uncertain and unpredictable incidents and their analysis requires the knowledge of the factors affecting them. Road and traffic accidents are defined by a set of variables which are mostly of discrete nature. The major problem in the analysis of accident data is its heterogeneous nature [1]. Thus heterogeneity must be considered during analysis of the data otherwise, some relationship between the data may remain hidden. Although, researchers used segmentation of the data to reduce this heterogeneity using some measures such as expert knowledge, but there is no guarantee
that this will lead to an optimal segmentation which consists of homogeneous groups of road accidents [2]. Therefore, cluster analysis can assist the segmentation of road accidents.

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

The idea that the purely phenomenological knowledge that we can extract by analysing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for individual patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. We propose in this position paper that big data analytics can be successfully combined with VPH technologies to produce robust and effective in silico medicine solutions. In order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. Such requirements are: working with sensitive data; analytics of complex and heterogeneous data spaces, including non-textual information; distributed data management under security and performance constraints; specialised analytics to integrate bioinformatics and systems biology information with clinical observations at tissue, organ and organisms scales; and

specialised analytics to define the “physiological envelope” during the daily life of each patient. These domain-specific requirements suggest a need for targeted funding, in which big data technologies for in silico medicine becomes the research priority.

 

Cloud computing offers a new way of service provision by re-arranging various resources over the Internet. The most important and popular cloud service is data storage. In order to preserve the privacy of data holders, data are often stored in cloud in an encrypted form. However, encrypted data introduce new challenges for cloud data deduplication, which becomes crucial for big data storage and processing in cloud. Traditional deduplication schemes cannot work on encrypted data. Existing solutions of encrypted data deduplication suffer from security weakness. They cannot flexibly support data access control and revocation. Therefore, few of them can be readily deployed in practice. In this paper, we propose a scheme to deduplicate encrypted data stored in cloud based on ownership challenge and proxy re-encryption. It integrates cloud data deduplication with access control. We evaluate its performance based on extensive analysis and computer simulations. The results show the superior efficiency and effectiveness of the scheme for potential practical deployment, especially for big data deduplication in cloud storage.

Big data takes many forms, including messages in social networks, data collected from various sensors, captured videos, and so on. Big data applications aim to collect and analyze large amounts of data, and efficiently extract valuable information from the data. A recent report shows that the amount of data on the Internet is about 500 billion GB. With the fast increase of mobile devices that can perform sensing and access the Internet, large amounts of data are generated daily.

In general, big data has three features: large volume, high velocity and large variety [1]. The International Data Corporation (IDC) predicted that the total amount of data generated in 2020 globally will be about 35 ZB. Facebook needs to process about 1.3 million TB of data each month. Many new data are generated at high velocity. For example, more than 2 million emails are sent over the Internet every second.

CLOUD computing [1] assembles large networks of virtualized ICT services such as hardware resources (such as CPU, storage, and network), software resources (such as databases, application servers, and web servers) and applications.In industry these services are referred to as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Mainstream ICT powerhouses such as Amazon, HP, and IBM are heavily investing in the provision and support of public cloud infrastructure. Cloud computing is rapidly becoming a popular infrastructure of choice among all types of organisations. Despite some initial security concerns and technical issues, an increasing number of organisations have moved their applications and services in to “The Cloud”. These applications range from generic word processing software to online healthcare. The cloud system taps into the processing power of virtualized computers on the back end, thus significantly speeding up the application for the user, which just pays for the used services.

The digitalization of mental health records and psychotherapy notes has made individual mental health data more readily accessible to a wide range of users including patients, psychiatrists, researchers, statisticians, and data scientists. However, increased accessibility of highly sensitive mental records threatens the privacy and confidentiality of psychiatric patients. The objective of this study is to examine privacy concerns in mental health research and develop a privacy preserving data analysis approach to address these concerns. In this paper, we demonstrate the key inadequacies of the existing privacy protection approaches applicable to use of mental health records and psychotherapy notes in recordsbased research. We then develop a privacy-preserving data analysis approach that enables researchers to protect the privacy of people with mental illness once granted access to mental health records. Furthermore, we choose a demonstration project to show the use of the proposed approach. This paper concludes by suggesting practical implications for mental health researchers and future research in the field of privacy-preserving data analytics.

Nowadays, cloud-based storage services are rapidly growing and becoming an emerging trend in data storage field. There are many problems when designing an efficient storage engine for cloud-based systems with some requirements such as big-file processing, lightweight meta-data, low latency, parallel I/O, deduplication, distributed, high scalability. Key-value stores

played an important role and showed many advantages when solving those problems. This paper presents about Big File Cloud (BFC) with its algorithms and architecture to handle most of problems in a big-file cloud storage system based on keyvalue store. It is done by proposing low-complicated, fixed-size meta-data design, which supports fast and highly-concurrent, distributed file I/O, several algorithms for resumable upload, download and simple data deduplication method for static data. This research applied the advantages of ZDB - an in-house keyvalue store which was optimized with auto-increment integer keys for solving big-file storage problems efficiently. The results can be used for building scalable distributed data cloud storage that support big-file with size up to several terabytes

In recent years, Big Data has changed how we do computing. Even though we have large scale infrastructure such as Cloud computing and several platforms such as Hadoop available to process the workloads, with Big Data there is a high level of uncertainty that has been introduced in how an application processes the data. Data in general comes in different formats, at different speed and at different volume. Processing consists of not just one application but several applications combined to form a workflow to achieve a certain goal. With data variation and at different speed, applications execution and resource needs will also vary at runtime. These are called dynamic workflows. One can say that we can just throw more and more resources during runtime. However this is not an effective way as it can lead to, in the best case, resource wastage or monetary loss and in the worst case, delivery of outcomes much later than when it is required. Thus, scheduling algorithms play an important role in efficient execution of dynamic workflow applications. In this paper, we evaluate several most commonly used workflow scheduling algorithms to understand which algorithm will be the best for the efficient execution of dynamic workflows.

Frequent itemset mining is an exploratory data mining technique that has fruitfully been exploited to extract recurrent co-occurrences between data items. Since in many application contexts items are enriched with weights denoting their relative importance in the analyzed data, pushing item weights into the itemset mining process, i.e., mining weighted itemsets rather than traditional itemsets, is an appealing research direction. Although many efficient in-memory weighted itemset mining algorithms are available in literature, there is a lack of parallel and distributed solutions which are able to scale towards Big Weighted Data. This paper presents a scalable frequent weighted itemset mining algorithm based on the MapReduce paradigm. To demonstrate its actionability and scalability, the proposed algorithm was tested on a real Big dataset collecting approximately 34 millions of reviews of Amazon items. Weights indicate the ratings given by users to the purchased items. The mined itemsets represent combinations of items that were frequently bought together with an overall rating above average.

The unprecedented growth in mobile device adoption and the rapid advancement of mobile technologies & wireless networks have created new opportunities in mobile marketing and adverting. The opportunities for Mobile Marketers and Advertisers include real-time customer engagement, improve customer experience, build brand loyalty, increase revenues, and drive customer satisfaction. The challenges, however, for the Marketers and Advertisers include how to analyze troves of data that mobile devices emit and how to derive customer engagement insights from the mobile data. This research paper addresses the challenge by developing Big Data Mobile Marketing analytics and advertising recommendation framework. The proposed framework supports both offline and online advertising operations in which the selected analytics techniques are used to provide advertising recommendations based on collected Big Data on mobile user's profiles, access behaviors, and mobility patterns. The paper presents prototyping solution design as well as its application and certain experimental results.

Users store vast amounts of sensitive data on a big data platform. Sharing sensitive data will help enterprises reduce the cost of providing users with personalized services and provide value-added data services. However, secure data sharing is problematic. This paper proposes a framework for secure sensitive data sharing on a big data platform, including secure data delivery, storage, usage, and destruction on a semi-trusted big data sharing platform. We present a proxy re-encryption algorithm based on heterogeneous ciphertext transformation and a user process protection method based on a virtual machine monitor, which provides support for the realization of system functions. The framework protects the security of users’ sensitive data effectively and shares these data safely. At the same time, data owners retain complete control of their own data in a sound environment for modern Internet information security.

In the era of big data, many users and companies start to move their data to cloud storage to simplify data management and reduce data maintenance cost. However, security and privacy issues become major concerns because third-party cloud service providers are not always trusty. Although data contents can be protected by encryption, the access patterns that contain important information are still exposed to clouds or malicious attackers. In this paper, we apply the ORAM algorithm to enable privacy-preserving access to big data that are deployed in distributed file systems built upon hundreds or thousands of servers in a single or multiple geo-distribu ted cloud sites. Since the ORAM algorithm would lead to serious access load unbalance among storage servers, we study a data placement problem to achieve a load balanced storage system with improved availability and responsiveness. Due to the NP-hardness of this problem, we propose a low-complexity algorithm that can deal with large-scale problem size with respect to big data. Extensive simulations are conducted to show that our proposed algorithm finds results close to the optimal solution, and significantly outperforms a random data placement algorithm.

Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, because the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-Based Encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also design policy updating algorithms for different types of access policies. The analysis show that our scheme is correct, complete, secure and efficient.

Spurred by service computing and cloud computing, an increasing number of services are emerging on the Internet. As a result, service-relevant data become too big to be effectively processed by traditional approaches. In view of this challenge, a Clustering-based Collaborative Filtering approach (ClubCF) is proposed in this paper, which aims at recruiting similar services in the same clusters to recommend services collaboratively. Technically, this approach is enacted around two stages. In the first stage, the available services are divided into small-scale clusters, in logic, for further processing. At the second stage, a collaborative filtering algorithm is imposed on one of the clusters. Since the number of the services in a cluster is much less than the total number of the services available on the web, it is expected to reduce the online execution time of collaborative filtering. At last, several experiments are conducted to verify the availability of the approach, on a real dataset of 6,225 mashup services collected from ProgrammableWeb.

With the explosive growth in data, using data mining techniques to mine association rules, and then to find valuable information hidden in big data has become increasingly important. Various existing data mmmg
techniques often through mining frequent itemsets to derive association rules and access to relevant knowledge, but with the rapid arrival of the era of big data, Traditional data mining algorithms have been unable to meet large data's analysis needs. In view of this, this paper proposes an adaptation to the big data mining parallel algorithms-MRPrePost. MRPrePost is a parallel algorithm based on Hadoop platform, which improves PrePost by way of adding a prefix pattern, and on this basis into the parallel design ideas, making MRPrePost algorithm can adapt to mining large data's association rnles. Experiments show that MRPrePost algorithm is more superior than PrePost and PFP in terms of performance, and the stability and scalability of algorithms are better.

A framework for maintaining security & preserving privacy for analysis of sensor data from smart homes, without compromising on data utility is presented. Storing the personally identifiable data as hashed values withholds identifiable information from any computing nodes. However the very nature of smart home data analytics is establishing preventive care. Data processing results should be identifiable to certain users responsible for direct care. Through a separate encrypted identifier dictionary with hashed and actual values of all unique sets of identifiers, we suggest re-identification of any data processing results. However the level of re-identification needs to be controlled, depending on the type of user accessing the results. Generalization and suppression on identifiers from the identifier dictionary before re-introduction could achieve different levels of privacy preservation. In this paper we propose an approach to achieve data security & privacy through out the complete data lifecycle:data generation/collection, transfer, storage, processing and sharing.

Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called ‘auditing-as-aservice’ at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarsegrained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions.

Applications Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficien-cy problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appro-priate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environ-ment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly im-proves the accuracy and scalability of service recommender systems over existing approaches.

The explosive growth of demands on big data processing imposes a heavy burden on computation, storage, and communication in data centers, which hence incurs considerable operational expenditure to data center providers. Therefore, cost minimization has become an emergent issue for the upcoming big data era. Different from conventional cloud services, one of the main features of big data services is the tight coupling between data and computation as computation tasks can be conducted only when the corresponding data is available. As a result, three factors, i.e., task assignment, data placement and data movement, deeply influence the operational expenditure of data centers. In this paper, we are motivated to study the cost minimization problem via a joint
optimization of these three factors for big data services in geo-distributed data centers. To describe the task completion time with the consideration of both data transmission and computation, we propose a two-dimensional Markov chain and derive the average task completion time in closed-form. Furthermore, we model the problem as a mixed-integer non-linear programming (MINLP) and propose an efficient solution to linearize it. The high efficiency of our proposal is validated by extensive simulation based studies.

The buzz-word big-data refers to the large-scale distributed data processing applications that operate on
exceptionally large amounts of data. Google’s MapReduce and Apache’s Hadoop, its open-source implementation, are the defacto software systems for big-data applications. An observation of the MapReduce framework is that the framework generates a large amount of intermediate data. Such abundant information is thrown away after the tasks finish, because MapReduce is unable to utilize them. In this paper, we propose Dache, a data-aware cache framework for big-data applications. In Dache, tasks submit their intermediate results to the cache manager. A task queries the cache manager before executing the actual computing work. A novel cache description scheme and a cache request and reply protocol are designed. We implement Dache by extending Hadoop. Testbed experiment results demonstrate that Dache significantly improves the completion time of MapReduce jobs.

With the increasing demand of social media, the security threats of these networks also increasing dramatically. Facebook having around 1.5 billion users are concerned with the security and privacy of its users. To make it more secure, the Facebook authority has introduced two-step verification system for login in one’s account. However, the second step of verification, i.e., verification code based security, can easily be breached with a simple trick. In this paper, we will show how we breached Facebook security and hacked accounts within 6 to 10 seconds only and we will propose a solution regarding this limitation.

With the increasing demand of social media, the security threats of these networks also increasing dramatically. Facebook having around 1.5 billion users are concerned with the security and privacy of its users. To make it more secure, the Facebook authority has introduced two-step verification system for login in one’s account. However, the second step of verification, i.e., verification code based security, can easily be breached with a simple trick. In this paper, we will show how we breached Facebook security and hacked accounts within 6 to 10 seconds only and we will propose a solution regarding this limitation.

Using password is, perhaps, still the most versatile method of securing secret and confidential  information, even though several recent studies have pointed out possibility of breaching it. A general trend of having different passwords for several user accounts of the same user (such as multiple email accounts, multiple social networking accounts, etc.) can barely overcome the possibility as users mostly prefer retaining similarity among own passwords, which results in the possibility of breaching almost all passwords once only one password gets breached. Consequently, several research studies attempted to

strengthen passwords. However, none of the studies is yet to get wide popularity for not being able to achieve a delicate balance between strength of password and user friendliness. To achieve this goal, we present a new password based authentication system in this paper. The proposed system is based on intermixing between a fixed text (conventional part of a password) and a free random text (newly added) at different pre-defined indices having different per-defined lengths. The addition of the free random text

adds an additional level of difficulty in breaching the password. We present different variants of our proposed system along with their possible attack models. We demonstrate strength of our proposed system through rigorous analytical formulation and numerical simulation. Besides, we confirm achieving a delicate balance between strength of the password and user friendliness through performing real user evaluation.

Researchers consider wireless nanosensor networks (WNSNs) as a revolutionary emerging network paradigm from the point of its diversified applications and contributions to the humanity. Existing research in this field is still in elementary stage and performance enhancement via designing protocol suit

represents a potential issue to address for this field. However, most of the studies in the literature mainly focus on lower layers, i.e., Physical and MAC layer protocols leaving upper layers such as Network layer and Transport layer protocols still unexplored. Therefore, in this paper, we explore performance enhancement in WNSNs via modifications in the existing network and transport layers protocols. In this paper, we devise a hierarchical AODV routing protocol and an acknowledgement-based UDP protocol

for WNSNs. We perform rigorous simulation in ns-2 to prove the efficacy and efficiency of our proposed mechanisms. Our simulation results reveal significant performance enhancement in wireless nanosensor networks for our proposed protocols.

Emotions are primarily thought of as mental experiences of body states, which are mostly shown in the face with precise and specific muscle patterns. It is, perhaps, the most critical attribute of living beings, and is extremely difficult to detect and generate artificially. Its detection always remains a well-explored classical problem. Existing approaches for detecting human emotions generally demand significant infrastructural overheads. Excluding these overheads, in this paper, we propose a much simpler way of emotion detection. To do so, We have induced different states of emotion through different multimedia

components, and then collected participants’ keystrokes (free text) and mouse usage data through a custom-developed survey. We have used several existing classifiers (KNN, KStar, RandomCommittee and RandomForest) and a newly proposed light-weight classifier namely Bounded K-means Clustering, to

analyze those usage data for different emotional states. Our analysis demonstrates that emotion can be detected from the usage data up to a certain level. Moreover, our proposed classifier enables the best detection of five emotional states namely happiness, inspiration, sympathy, disgust, and fear compared to

other existing classifiers. Besides, the analysis also reveals that user identification through usage dynamics does not result in a good level of accuracy when usage gets influenced by different

emotional states.

Knowledge discovery from various health data repositories requires the incorporation of healthcare data from diversified sources. Maintaining record linkage during the integration of medical data is an important research issue. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), universal patient identifier (UPI), health insurance number, etc. These solutions cannot be used correctly for record linkage of health data of developing countries because of missing data, ambiguity in patient identification, and high amount of noise in patient information. Also, identifiable health data in electronic health repositories may produce a significant risk to patient privacy and also make the health information systems security vulnerable to hackers. In this paper, we have analyzed the practical problems of collecting and integrating healthcare data in Bangladesh for developing national  health data warehouse. We have proposed a privacy preserved secured record linkage architecture that can support constrained health data of developing countries such as Bangladesh. Our technique can anonymize identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. Experimental results show that our proposed method successfully linked records with acceptable accuracy for noisy data in the absence of any standard ID like SSN.

To solve the key management related problems encountered in symmetric as well as asymmetric key based schemes, recently a public key based scheme known as Certificateless Effective Key Management protocol (CL-EKM) has been proposed for dynamic Wireless Sensor Networks (WSNs). In spite of showing numerous advantages over the previously proposed schemes, this protocol shows some critical

limitations. One among these is the method of relying on unicast transmission mode to transmit messages from the Base Station (BS) to all cluster heads in the network. This is because when the

network grows in size or when the number of messages to be transmitted at a given time is large, this would cause severe negative impact on the overall performance. Hence, in this paper, we consider the optimization problem of the protocol and propose a solution which enhances CL-EKM by avoiding

intensive use of encryption and unicast operations that reduces the energy and delay associated with the communications between the BS and the cluster heads. The performance gains are depicted in the presented results.

The application scope of VPN is increasing day by day as the organizations are creating private networks through public Internet using VPN tunneling instead of leased line. VPN protocols are classified into site-to-site and remote access VPN which exhibits different set of characteristics in terms of security mechanism. But there is no VPN preferences based on the organizational application requirements. In this paper, different VPN tunneling protocols like GRE, IPSec, PPTP and L2TP with IPSec are analyzed to measure the performance in terms of throughput, RTT, Jitter and security parameters. The results

exhibits that, GRE is preferable for delay and bandwidth sensitive application in context of site to site VPN and L2TP is more effective than PPTP for remote access VPN.

Bringing Big data technologies into agriculture presents a significant challenge; at the same time, this technology contributes effectively in many countries’ economic and social development. In this work, we will study environmental data provided by precision agriculture information technologies, which represents a crucial source of data in need of being wisely managed and analyzed with appropriate methods and tools in order to extract the meaningful information. Our main purpose through this paper is to propose an effective Big data architecture based on profiling system which can assist (among others) producers, consulting companies, public bodies and research laboratories to make better decisions by providing them real time data processing, and a dynamic big data service composition method, to enhance and monitor the agricultural productivity. Thus, improve their traditional decision making process, and allow better management of the natural resources.

Cloud computing is the latest technology in the field of distributed computing. It provides various online and on-demand services for data storage, network services, platform services and etc. Many organizations are unenthusiastic to use cloud services due to data security issues as the data resides on the cloud services provider’s servers. To address this issue, there have been several approaches applied by various researchers worldwide to strengthen security of the stored data on cloud computing. The Bi-directional DNA Encryption Algorithm (BDEA) is one such data security techniques. However, the existing technique focuses only on the ASCII character set, ignoring the non-English user of the cloud computing. Thus, this proposed work focuses on enhancing the BDEA to use with the Unicode characters

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain
valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to reauthenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public key encryption with keyword search (PEKS) which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional PEKS framework suffers from an inherent insecurity called inside keyword guessing attack (KGA) launched by the malicious server. To address this security vulnerability, we propose a new PEKS framework named dual-server PEKS (DS-PEKS). As another main contribution, we define a new variant of the smooth projective hash functions (SPHFs) referred to as linear and homomorphic SPHF (LH-SPHF). We then show a generic construction of secure DS-PEKS from LH-SPHF. To illustrate the feasibility of our new framework, we provide an efficient instantiation of the general framework from a Decision Diffie–Hellman-based LH-SPHF and show that it can achieve the strong security against inside the KGA.

Scheduling and broadcasting of data through network tunnels is always a big challenge in closed network topologies. Each and individual tunnel or part of network will be having its own capacity to transmit and receive the packets. Adoptive and open networks are easy to transmit the data but the challenges will occur in synchronization of data transmission among them. So clustering, tracking, log maintenance of the data transmission among the channels or tunnels and retransmission with respect energy levels and synchronization can be achieved by incremental tracking retransmission (ITR[1]) approach. Energy levels will be monitored by network monitor and assigns scheduling depends on the network capacity of the available methodologies. Here the three methodologies are 1.Memory less channels[2] , 2. Modulated
channel[3], 3.Joint and uniform scheduling[4] for data transmission with respect to scheduling. Considerable throughput criteria is framed with incremental flow with our work to end up fair and best accuracy levels. This ITR method is totally unique in open networks. Here open networks means which can adopt with legacy and other adoptive open networks in tunnelling or bridge level transmission. The packet buffering and delivery is always depends on previous cluster or next cluster and chance of losing the packets. So to overcome our work is practically implemented in chunks mechanism. Totally 3 or more chunks will be framed as clusters which acts as incremental growth in transmission with respect to losing of the packets. The central frame work which works as auto deployment methodology to track the tunnels. The loss of frequency is traceable using this frame work and adopts the lost and non lost packets addresses and flushes to next level to fulfil ITR method. The practical implementation depends on asynchronous services to roll back to any level/cluster. The feasible transmission is achieved in incremental level of clusters which will get the log or track information about the data from central frame work.

To share the data in between neighbor nodes in established or fixed MANET is a big challenge. Always displaced movements MANET nodes are unpredictable with respect to their moving places in case of sharing data. And data sharing is late and sometimes hard in between deferent networks. And also compromised nodes may take advantage to take and hide the data. So to overcome these scenario we propose a new approach called SMN (Smart movement notice), EDT (Efficient data Transfer). And to transfer the data in encoded format we propose a new algorithm ROTA (Rotation orient transfer analog). All these techniques can be used across the MANETs in deferent networks. The data can be shared via non compromised hash technique in ROTA technique. All the techniques are inter related with one approach of data transfer in efficient manner. The neibour nodes displacements are available all the times to all current network nodes and also root or master node. The master node is the key node to transfer the data to other networks in encoded formats. The data can be large and also feasible formats to transfer to legacy networks.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis
demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

We propose a simple method for improving the security of  hashed passwords: the maintenance of additional “honey- words” (false passwords) associated with each user’s account. An adversary who steals a file of hashed passwords and in- verts the hash function cannot tell if he has found the password or a honeyword. The attempted use of a honeyword for login sets off an alarm. An auxiliary server (the “hon- eychecker”) can distinguish the user password from honey- words for the login routine, and will set off an alarm if a honeyword is submitted.

Security is an important issue in sensor networks. Manyapplications in military, distributed information gatheringetc., demand for Secure Group Communication (SGC) insensor networks. The SGC requires common network-widegroup key for confidentiality of control messages and datareports. The group key should be updated when a node iscompromised. In this paper we propose a new key management.

scheme for group key computation and distributionwhich is based on tree structure. The proposed scheme minimizesstorage as well as communication and computationcost of end user (i.e., sensor nodes). The complex encryption/decryption operations used to distribute new group keywhenever a node is compromised are replaced by one wayhash functions and simple XOR operations.Keywords: Secure Group Communication, SensorNode, Hash Function, Group Key.

File sharing applications in mobile ad hoc networks (MANETs) have attracted more and more attention in recent years. The efficiency of file querying suffers from the distinctive properties of such networks including node mobility and limited communication range and resource. An intuitive method to alleviate this problem is to create file replicas in the network. However, despite the efforts on file replication, no research has focused on the global optimal replica creation with minimum average querying delay. Specifically, current file replication protocols in mobile ad hoc networks have two shortcomings. First, they lack a rule to allocate limited resources to different files in order to minimize the average querying delay. Second, they simply consider storage as available resources for replicas, but neglect the fact that the file holders’ frequency of meeting other nodes also plays an important role in determining file availability.

Actually, a node that has a higher meeting frequency with others provides higher availability to its files. This becomes even more evident in sparsely distributed MANETs, in which nodes meet disruptively. In this paper, we introduce a new concept of resource for file replication, which considers both node storage and meeting frequency. We theoretically study the influence of resource allocation on the average querying delay and derive a resource allocation rule to minimize the average querying delay. We further propose a distributed file replication protocol to realize the proposed rule. Extensive trace-driven experiments with synthesized traces and real traces show that our protocol can achieve shorter average querying delay at a lower cost than current replication protocols.

Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. We are especially interested in the insider-attack case, whereby malicious nodes that are part of the route exploit their knowledge of the communication context to selectively drop a small amount of packets critical to the network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to exploit the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a homomorphic linear authenticator (HLA) based public auditing architecture that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This construction is privacy preserving, collusion proof, and incurs low communication and storage overheads. To reduce the computation overhead of the baseline scheme, a packet-block-based mechanism is also proposed, which allows one to trade detection accuracy for lower computation complexity. Through extensive
simulations, we verify that the proposed mechanisms achieve significantly better detection accuracy than conventional methods such as a maximum-likelihood based detection.

Cloud security is one of most important issues that has attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as multistep exploitation, low-frequency vulnerability scanning, and compromising identified vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised zombies. Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from being compromised in the cloud, we propose a multiphase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE, which is built on attack graph-based analytical models and reconfigurable virtual network-based countermeasures. The proposed framework leverages OpenFlow network programming APIs to build a monitor and control plane over distributed programmable virtual switches to significantly improve attack detection and mitigate attack consequences. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.

Vehicular ad hoc networks (VANETs) adopt the Public Key Infrastructure (PKI) and Certificate Revocation Lists (CRLs) for their security. In any PKI system, the authentication of a received message is performed by checking if the certificate of the sender is included in the current CRL, and verifying the authenticity of the certificate and signature of the sender. In this paper, we propose an Expedite Message Authentication Protocol (EMAP) for VANETs, which replaces the time-consuming CRL checking process by an efficient revocation checking process. The revocation check process in EMAP uses a keyed Hash Message Authentication Code ðHMACÞ, where the key used in calculating theHMAC is shared only between nonrevoked On-Board Units (OBUs). In addition, EMAP uses a novel probabilistic key distribution, which enables nonrevoked OBUs to securely share and update a secret key. EMAP can significantly decrease the message loss ratio due to the message verification delay compared with the conventional authentication methods employing CRL. By conducting security analysis and performance evaluation,EMAP is demonstrated to be secure and efficient.

Mobile ad hoc networks (MANETs) have attracted much attention due to their mobility and ease of deployment. However, the wireless and dynamic natures render them more vulnerable to various types of security attacks than the wired networks. The major challenge is to guarantee secure network services. To meet this challenge, certificate revocation is an important integral component to secure network communications. In this paper, we focus on the issue of certificate revocation to isolate attackers from further participating in network activities. For quick and accurate certificate revocation, we propose the Cluster-based Certificate Revocation with Vindication Capability (CCRVC) scheme. In particular, to improve the reliability of the scheme, we recover the warned nodes to take part in the certificate revocation process; to enhance the accuracy, we propose the threshold-based mechanism to assess and vindicate warned nodes as legitimate nodes or not, before recovering them. The performances of our scheme are evaluated by both numerical and simulation analysis. Extensive results demonstrate that the proposed certificate revocation scheme is effective and efficient to guarantee secure communications in mobile ad hoc networks.

Communication in Mobile Ad hoc network is done over a shared wireless channel with no Central Authority (CA) to monitor. Responsibility of maintaining the integrity and secrecy of data, nodes in the network are held responsible. To attain the goal of trusted communication in MANET (Mobile Ad hoc Network) lot of approaches using key management has been implemented. This work proposes a composite identity and trust based model (CIDT) which depends on public key, physical identity, and trust of a node which helps in secure data transfer over wireless channels. CIDT is a modified DSR routing protocol for achieving security. Trust Factor of a node along with its key pair and identity is used to authenticate a node in the network. Experience based trust factor (TF) of a node is used to decide the authenticity of a node. A valid certificate is generated for authentic node to carry out the communication in the network. Proposed method works well for self certification scheme of a node in the network.

Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users. Many LBS providers use users’ location information to offer them convenience and useful functions. However, the LBS could greatly breach personal privacy because location itself contains much information. Hence, preserving location privacy while achieving utility from it is still an challenging question now. This paper tackles this non-trivial challenge by designing a suite of novel fine-grained Privacy-preserving Location Query Protocol (PLQP). Our protocol allows different levels of location query on encrypted location information for different users, and it is efficient enough to be applied in mobile platforms.

Wireless sensor network is a collection of large number of sensor nodes that are deployed in large number to monitor the environment. There is a great technological advancement in wireless sensor network during last few years. Due to low-cost, small-size, nature of Wireless Sensor Networks (WSNs), it allows them to sense the information in various hostile environments (e.g. military surveillance, battlefield). So, to fully achieve the capacity of WSNs, sensor nodes need to cooperate in the collection and must disseminate topology information. These sensor nodes specifically operate in a multihop routing. Sensor network in muiltihop routing faces a variety of risks which is also
due to the harsh operating environments. In this paper a fuzzy based approach is introduced which will enhance the routing security and reliability in WSNs.

Mobile cloud computing (MCC) is an emerging trend which combines the benefits of cloud computing with the mobile devices. This new technology not only offers tremendous computing power and storage to the mobile devices with limited processing and storage capabilities but also increases the affordability and reliability. Despite providing various benefits, MCC is still in its early stages in providing trust guarantees to a user. Location-Based Services (LBS), on the other hand, are those services which operate on a users location to provide him/her services such as finding nearby restaurants, hospitals, bus terminal and ATMs, to name a few. While a users location is mandatory for LBS to work, it imposes serious threats to the users privacy. In this paper we propose a privacy preserving cloud-based computing architecture for using location-based services. On one hand, our architecture provides a secure mechanism for using LBS services anonymously while on the other hand it utilizes untrusted but fast and reliable cloud services for its implementation in an efficient and effective manner. Moreover, we provide various attack scenarios and show that how our architecture preserves the privacy of the user and is difficult to compromise.

The problem of efficiently and securely broadcasting to a remote cooperative group occurs in many newly emerging networks. A major challenge in devising such systems is to overcome the obstacles of the potentially limited communication from the group to the sender, the unavailability of a fully trusted key generation center, and the dynamics of the sender. The existing key management paradigms cannot deal with these challenges effectively. In this paper, we circumvent these obstacles and close this gap by proposing a novel key management paradigm. The new paradigm is a hybrid of traditional broadcast encryption and group key agreement. In such a system, each member maintains a single public/secret key pair. Upon seeing the public keys of the members, a remote sender can securely broadcast to any intended subgroup chosen in an ad hoc way. Following this model, we instantiate a scheme that is proven secure in the standard model. Even if all the nonintended members collude, they cannot extract any useful information from the transmitted messages. After the public group encryption key is extracted, both the computation overhead and the communication cost are independent of the group size. Furthermore, our scheme facilitates simple yet efficient member deletion/addition and flexible rekeying strategies. Its strong security against collusion, its constant overhead, and its implementation friendliness without relying on a fully trusted authority render our protocol a very promising solution to many applications.

Back-pressure-based adaptive routing algorithms where each packet is routed along a possibly different path have been extensively studied in the literature. However, such algorithms typically result in poor delay performance and involve high implementation complexity. In this paper, we develop a new adaptive routing algorithm built upon the widely studied back-pressure algorithm. We decouple the routing and scheduling components of the algorithm by designing a probabilistic routing table that is used to route packets to per-destination queues. The scheduling decisions in the case of wireless networks are made using counters called shadow queues. The results are also extended to the case of networks that employ simple forms of network coding. In that case, our algorithm provides a low-complexity solution to optimally exploit the routing-coding tradeoff.

Handling traffic dynamics in order to avoid network congestion and subsequent service disruptions is one of the key tasks performed by contemporary network management systems. Given the simple but rigid routing and forwarding functionalities in IP base environments, efficient resource management and control solutions against dynamic traffic conditions is still yet to be obtained. In this article, we introduce AMPLE — an efficient traffic engineering and routing topologies for long term operation through the optimized setting of link weights. Based on these diverse paths, adaptive traffic control performs intelligent traffic splitting across individual routing topologies in reaction to the monitored network dynamics at short timescale. According to our evaluation with real network topologies and traffic traces, the proposed system is able to cope almost optimally with unpredicted traffic dynamics and, as such, it constitutes a new proposal for achieving better quality of service and overall network performance in IP networks. Management system that performs adaptive traffic control by using multiple virtualized routing topologies. The proposed system consists of two complementary components: offline link weight optimization that takes as input the physical network topology and tries to produce maximum routing path diversity across multiple virtual

In the Internet era, online platforms are the most convenient means for people to share and retrieve knowledge. Social media enables users to easily post their opinions and perspectives regarding certain issues. Although this convenience lets the internet become a treasury of information, the overload also prevents user from understanding the entirety of various events. This research aims at using text mining techniques to explore public opinion contained in social media by analyzing the reader’s emotion towards pieces of short text. We propose Public Opinion Keyword Embedding (POKE) for the presentation of short texts from social media, and a vector space classifier for the categorization of opinions. The experimental results demonstrate that our method can effectively represent the semantics of short text public opinion. In addition, we combine a visualized analysis method for keywords that can provide a deeper understanding of opinions expressed on social media topics.

The rapid increase in mountains of unstructured textual data accompanied by proliferation of tools to analyse them has opened up great opportunities and challenges for text mining research. The automatic labelling of text data is hard because people often express opinions in complex ways that are sometimes difficult to comprehend. The labelling process involves huge amount of efforts and mislabelled datasets usually lead to incorrect decisions. In this paper, we design a framework for sentiment analysis with opinion mining for the case of hotel customer feedback. Most available datasets of hotel reviews are not labelled which presents a lot of works for researchers as far as text data pre-processing task is concerned. Moreover, sentiment datasets are often highly domain sensitive and hard to create because sentiments are feelings such as emotions, attitudes and opinions that are commonly rife with idioms, onomatopoeias, homophones, phonemes, alliterations and acronyms. The proposed framework is termed sentiment polarity that automatically prepares a sentiment dataset for training and testing to extract unbiased opinions of hotel services from reviews. A comparati ve analysis was established with Naïve Bayes multinomial, sequential minimal optimization, compliment Naïve Bayes and Composite hypercubes on iterated random projections to discover a suitable machine learning algorithm for the classification component of the framework.

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.

System Architecture

Project Overview Marking the keywords without knowing the dictionary is a big issue and that too with relative data. Here the data will be having offline dictionary for proper marking with summarization. So here the data marking is done with respect to offline dictionary and once the same data sent to server where the machine learning happening and point summarization updates and will be changed according to the future dictionary.

System Requirement
Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse


Hardware Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings,Core java
IDE : Netbeans.

Below code is for highlighting the content in the document.
Highlighter highlighter = mainTextContent.getHighlighter();
HighlightPainter painter = new DefaultHighlighter.DefaultHighlightPainter(Color.YELLOW);
mainTextContent.setHighlighter(highlighter);

<p>The GPS signals received from smart phone device will be used to monitor the person when he is driving .The coordinates received from the GPS is stored in the Cloud. This data is further used to monitor the speed at which the person is driving. Information is maintained about each person and if the person crosses a threshold speed he is categorized as a driver. This way motor insurance companies have the potential to provide customized solutions to their clients.</p>

System Architecture Project Overview Fetch the data of past data of the drivers past history and by applying the naive and novel approaches the driver's rashness prediction can happen in peak and non peak hours. Both algorithms compares for the accuracy of the prediction.
System Requirement

Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Software Requirement

Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings, Core java.
IDE : Netbeans.

Below code is for comparison of driver prediction
long clusterProcessTime = 0;
long naiveProcessTime = 0;
int clusterComparativeFreq = 0;
int naiveComparativeFreq = 0;

<p> Retail marketers are constantly looking for ways to improve the effectiveness of their campaigns. One way to do this is to target customers with the particular offers most likely to attract them back to the store and to spend more time and money on their next visit. Demographic market segmentation is an approach to segmenting markets. A company divides the larger market into groups based on several defined criteria. Age, gender, marital status, occupation, education and income are among the commonly considered demographics segmentation criteria. A sample case study has been done in order to explain the theory of segmentation applied on a Turkish supermarket chain. The purpose of this case study is to determine dependency on products and shopping habits. Furthermore forecast sales determine the promotions of products and customer profiles. Association rule mining was used as a method for identifying customers buying patterns and as a result customer profiles were determined. Besides association rules, interesting results were found about customer profiles, such as “What items do female customers buy?” or “What do consumers(married and 35-45 aged) prefer mostly?”. For instance, female customers purchase feta cheese with a percentage of 60% whereas male customers purchase tomato with a percentage of 46%. Regarding to customers age, 65 and older customers purchase tea with a percentage of 58%, and customers aged between 18- 25 preferred pasta with a percentage of 57%

System Architecture Project Overview
Reading the customers information with id and grouping with respect to countries and association with products and shows the accuracy of the association of customer association with products.
System Requirement
Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings, Core java.
IDE : Netbeans.


The below code is to find the start time of the customers
long startTIme = System.currentTimeMillis();
ArrayList customerId = new ArrayList();
customerId.clear();
customerId = (ArrayList)at7.clone();
Set set = new HashSet(customerId);
customerId.clear();
customerId.addAll(set);

<p>Traditional cluster ensemble approaches have three limitations: (1) They do not make use of prior knowledge of the datasets given by experts. (2) Most of the conventional cluster ensemble methods cannot obtain satisfactory results when handling high dimensional data. (3) All the ensemble members are considered, even the ones without positive contributions. In order to address the limitations of conventional cluster ensemble approaches, we first propose an -supervised clustering ensemble framework (ISSCE) which makes use of the advantage of the random subspace technique, the constraint propagation approach, the proposed incremental ensemble member selection process, and the normalized cut algorithm to perform high dimensional data clustering. The random subspace technique is effective for handling high dimensional data, while the constraint propagation approach is useful for incorporating prior knowledge. The incremental ensemble member selection process is newly designed to judiciously 
remove redundant ensemble members based on a newly proposed local cost function and a global cost function, and the normalized cut algorithm is adopted to serve as the consensus function for providing more stable, robust, and accurate results. Then, a measure is proposed to quantify the similarity between two sets of attributes, and is used for computing the local cost function in ISSCE. Next, we analyze the time complexity of ISSCE theoretically. Finally, a set of nonparametric tests are adopted to compare multiple semisupervised clustering ensemble approaches over different datasets. The experiments on 18 real-world datasets, which include six UCI datasets and 12 cancer gene expression profiles, confirm that ISSCE works well on datasets</p>

Project Overview After acquiring the twitter data and by taking the dictionaries(+ve and -ve) analisys will happen to prediction and +ve and -ve frequency for the recommendations.

System Configuration

Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse

Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : swings, core java
IDE : Netbeans

Below code shows how to load the tweets
FileInputStream fisP = new FileInputStream("text\\keywordP.txt");
byte bb1[] = new byte[fisP.available()];
fisP.read(bb1);
fisP.close();
String allPString = new String(bb1);

Data analysis is nothing new. Even before computers were used, information gained in the course of business or other activities was reviewed with the aim of making those processes more efficient and more profitable. These were, of course, comparatively small-scale undertakings given the limitations posed by resources and manpower; analysis had to be manual and was slow by modern standards, but it was still worthwhile.

Predictive analytics in sport is not new, but it’s a novel move in cricket. As it has been implemented in many other sports majorly in NBA basketball games, baseball and American football. So introducing it into the field of cricket is a new challenge which would definitely prove to be as successful and efficient as it has been in other fields. Taking the example of 2015 ICC Cricket World Cup, which was the most digitally advanced tournament in the history of cricket as the predictions that were made proved to be right. This served to be a motivation for this project.

System Architecture

Project Overview
Initially we fetch the 2 opponent teams and based on their past career and by applying mean weighted vector, players will be recommended to play as bats men and bowlers in both teams

System Requirement

Hardware Requirement
Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse

Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Html,JSp.
IDE : Netbeans.

Below code shows how to fetch the names of the different player from team.
try{
String names[] = request.getParameterValues("tplanyer");
allSelectedTNames.clear();
for(int i=0;i allSelectedTNames.add(names[i]);
}

As more and more applications produce streaming data, clustering data streams has become an important technique for data and knowledge engineering. A typical approach is to summarize the data stream in real-time with an online process into a large number of so called micro-. Micro- represent local density estimates by aggregating the information of many data points in a defined area. On demand, a (modified) conventional clustering algorithm is used in a second offline step to recluster the micro- into larger final . For reclustering, the centers of the micro- are used as pseudo points with the density estimates used as their weights. However, information about density in the area between micro- is not preserved in the online process and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro- (e.g., uniform or Gaussian). This paper describes DBSTREAM, the first micro-cluster-based online clustering component that explicitly captures the density between micro- via a shared density graph. The density information in this graph is then exploited for reclustering based on actual density between adjacent micro-. We discuss the space and time complexity of maintaining the shared density graph. Experiments on a wide range of synthetic and real data sets highlight that using shared density improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller micro- to achieve comparable results.

System Architecture

Project Overview Fetch medical (thyroid) data and cluster the data with respect the disease and later it recommends the associated doctors and prediction of best suitable doctor of the selected disease. Processing is happening over 5000 records.

System Requirement
Hardware Requirement Processor - Dual Core
Speed - 1.1 G Hz
RAM - 512 MB (min)
Hard - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse

Software Requirement
Operating System : Windows xp,7,8
Front End : Java 7
Technology : Swings, Core java.
IDE : Netbeans.

Sample Code

String disName = Disease.getSelectedItem ().toString();
ArrayList indexes= new ArrayList();
indexes.clear();
for(int i=0;i {
if( decease.get(i).toString().equals(disName))
{
mainClusterIndexes.add(i);
indexes.add(i);
}

The above code show how to select the different disease names.

Since the introduction of process model for knowledge discovery [1], the importance of data mining methods is dramatically increased making this research area relevant and challenging to extract actionable knowledge from raw data. Data mining techniques discover useful information by analyzing

data from multiple perspectives to deal with problems linked to information retrieval in different application domains. This process can be endowed with external data that may lead to additional insights in a way that the user can benefit from it. For example, when dealing with politic data, an analyst presumably knows the name of the prime minister of worlds states. So, she could add a variable “prime-minister” to datasets under exploration in order to extract pertinent and useful information. That example glimpses the enormous benefits that result from integrating raw data with the large amount of information related to new research domains such as big data, internet of things, cloud computing etc. It is well know that

this information is available on the Web according to multiple modalities, multiple resources and multiple formats.

This paper studies the feasibility of privacy preserving data mining in epidemiological study. As for the datamining algorithm, we focus to a linear multiple regression that can be used to identify the most significant factors among many possible variables, such as the history of many diseases. We try

to identify the linear model to estimate a length of hospital stay from distributed dataset related to the patient and the disease information. In this paper, we have done experiment using the real medical dataset related to stroke and attempt to apply multiple regression with six predictors of age, sex, the medical scales, e.g., Japan Coma Scale, and the modified Rankin Scale. Our contributions of this paper include (1) to propose a practical privacy-preserving protocols for linear multiple regression with

vertically partitioned datasets, and (2) to show the feasibility of the proposed system using the real medical dataset distributed into two parties, the hospital who knows the technical details of diseases during the patients are in the hospital, and the local government who knows the residence even after the patients left hospital. (3) to show the accuracy and the performance of the PPDM system which allows us to estimate the expected processing time with arbitrary number of predictors.

Fraud detection is a scenario applicable to many industries such as banking and financial sectors, insurance, healthcare, government agencies and law enforcement and more.There has been a drastic increase in recent years ,pushing fraud detection more important than ever.Hundreds of millions of dollars are lost to fraud every year.Upcoding fraud is one such fraud in which a service provider acquires additional financial gain by coding a service by upgrading it even though the lesser service has been performed.Incorporating artificial intelligence with data mining and statistics help to anticipate and detect these frauds and minimize costs.Using sophisticated data mining tools ,millions of transcations can be searched to spot patterns and detect fraudulent transactions.This paper gives an insight into the various datamining tools which are efficient in detecting upcoding frauds especially in the healthcare insurance sector in India.

The Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a

strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.

 

For the reason that the algorithm PFIM needs to scan database repeatedly,to produce a great deal of redundant candidate itemset,and to compute more time-complexity of frequent probability, an improved algorithm UPro-Eclat which is based on PFIM and Eclat is proposed.It uses a vertical mining method which is extension-based,adds probabilistic information in Tid ,builds recursively the subset of search

tree,and mines probabilistic frequent pattern by depth-first traversa. The algorithm UPro-Eclat can swiftly find probabilistic frequent itemset rather than compute their probability in each possible world.

This short survey paper attempts to provide an overview of the most recent research works on the popular politics domain within the framework of the Twitter social network. Given both the political turmoil that arouse at the end of 2016 and early 2017, and the increasing popularity of social networks in

general, and Twitter, in particular, we feel that this topic forms an attractive candidate for fellow data mining researchers that came into sight over the last few months. Herein, we start by presenting a brief overview of our motivation and continue with basic information on the Twitter platform, which constitutes two clearly identifiable components, namely as an online news source and as one of the most popular social networking sites. Focus is then given to research works dealing with sentiment analysis in political topics and opinion polls, whereas we continue by reviewing the Twittersphere from the computational social science point of view, by including behavior analysis, social interaction and social influence identification methods and by discerning and discriminating its useful types within the social network, thus

envisioning possible further utilization scenarios for the collected information. A short discussion on the identified conclusions and a couple of future research directions concludes the survey.

Temporal or spatio-temporal sequential pattern discovery is a well-recognized important problem in many domains likeseismology, criminology and finance. The majority of the current approaches are based on candidate generation which necessitates parameter tuning namely,definition of a neighborhood, an interest measure and a threshold value to evaluate candidates.  However, their performance is limited as the success of these methods relies heavily on parameter settings. In this paper, we propose an algorithm which uses a nonparametric stochastic de-clustering procedure and a multivariate Hawkes model to define triggering relations within and among the event types and employs the estimated model to extract significant triggering patterns of event types. We tested the proposed method with real and synthetic data sets exhibiting different characteristics. The method gives good results that are comparable with the methods based on candidate generation in the literature.

Graph data analysis is one of the upcoming methodologies in various niches of computer science. Traditionally for storing, retrieving and experimenting test data, researchers start with mysql database which is more approachable and easier to build their test experimentation platform. These test bed mysql databases will store data in the form of rows and columns, over which various SQL queries are performed. At times when the structure and size of dataset changed, these traditional mysql databases become inefficient in storing and retrieving of data. When the structure of dataset changes from row-column to graph representation, mysql database based querying and analysis become inefficient. The

internal representation of data is changed to key-value pairs, more often the data in an unstructured format, which prompted the researchers to think about other databases which can achieve faster retrieval and mining over the dataset. This paper explores the approach of NoSql query design and analysis of

different datasets, particularly a proteome-protein dataset over a renowned graph database, Neo4j. The mode of experiments involve the evaluation of NoSql query execution on datasets vary in the number of nodes and relationships between them. It also emphasises the process of mining large graphs with meaningful queries based on a NoSql Query language called Cypher.

 

Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data . There are many data mining techniques like clustering, classification, association analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease(CKD) using classification techniques like Naive Bayes and Artificial Neural Network(ANN). The
experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.

CHRONIC KIDNEY DISEASE ANALYSIS USING DATA MINING CLASSIFICATION TECHNIQUES

Advent in microarray technology enables the researchers of computational biology to apply various bioinformatics paradigms on gene expression data. But microarray gene expression data, obtained as a matrix has much larger number of genes as rows than number of samples (columns representing time points / disease states) which makes many bioinformatics jobs critical. In this direction a subset of
genes are selected from the large noisy dataset so that their features are distinguishable between different types of samples (normal / diseased). Here, in this paper, the gene selection process
is performed by sample classification using k Nearest Neighbor (k-NN) method. Comparing normal and preeclampsia affected microarray gene expression samples, collected from human placentas; we have selected a set of genes which may be termed as critical for a disease named Preeclampsia, a common complication during pregnancy causing hypertension and proteinuria. Both the normal and diseased dataset contain 25000 genes (rows) having 75 samples (columns) and we have selected 30 genes as disease-critical genes. We have applied two metaheuristic algorithms, namely, Simulated Annealing (SA) and
Particle Swarm Optimization (PSO). Sample classification of normal and preeclampsia shows high fitness (number of samples properly classified). Here, out of 150 (75 normal + 75 diseased) samples, 80-90 samples are properly classified. The number of samples properly classified, denotes the fitness of a solution. So, achieved solution here is of good quality. In our experiments, PSO outperformed SA in respect of best fitness and SA defeated PSO in average fitness.

Outlier detection has been used for centuries to detect, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behavior, fraudulent behavior, human error, instrument error or simply through natural deviations in populations. These anomalous patterns are usually called outliers, noise, anomalies, exceptions, faults, defects, errors in different application domains.  The proposed method analyses the NSL-KDD dataset of various clustering algorithms like KNN, wards and NN to find outliers from the each resulting clusters and then apply different outlier detection techniques which are of distance based, density based, soft computing based to the resultant dataset by using data mining WEKA tool. And finally the results are analyzed for the proposed method which performs with high accuracy in less time complexity.

DSeNiR is proposed in this project in order to manage the encrypted network intrusion on a cloud storage Hbase and Hadoop are Utilized in this work. The objective is to provide an API for any Ni system to upload/download the encrypted network intrusion data from a cloud storage. The DSeNiR resolve the name node memory issues of HDFS. When storing a lot of small files by classifying the encrypted network intrusion data into small and large files. The small files will be handled by HBase schema that is proposed in this work. The memory consumption and the processing time of the proposed DSeNiR are evaluated using real data sets collected from various network intrusions.

DSeNiR is proposed in this project in order to manage the encrypted network intrusion on a cloud storage Hbase and Hadoop are Utilized in this work. The objective is to provide an API for any Ni system to upload/download the encrypted network intrusion data from a cloud storage. The DSeNiR resolve the name node memory issues of HDFS. When storing a lot of small files by classifying the encrypted network intrusion data into small and large files. The small files will be handled by HBase schema that is proposed in this work. The memory consumption and the processing time of the proposed DSeNiR are evaluated using real data sets collected from various network intrusions.

A college timetable is a temporal arrangement of a set of lectures and classrooms in which all given constraints are satisfied. Creating such timetables manually is complex and time-consuming process. By automating this process with computer assisted timetable generator can save a lot of precious time of administrators who are involved in creating and managing course timetables. Since every college has its own timetabling problem, the commercially available software packages may not suit the need of every college. Hence we have developed practical approach for building lecturecourse timetabling system, which can be customized to fit to any colleges timetabling problem.

 

This project introduces a practical timetabling algorithm capable of taking care of both strong and weak constraints effectively, used in an automated timetabling system. So that each teacher and student can view their timetable once they are finalized for a given semester but they can’t edit them.

Generation of an Invoice bill of selected products from a different websites and generates the bill through PDF format and forwards the bill to gmail.

The main aim and objective was to plan and Software application for any domain. We have to apply the best software engineering practice for web application. As a Software application developer I was asked to developed an “Faculty Feedback System” using Core Java. “Faculty Feedback System” This system is generally used by four kinds of users Student, Faculty, Head of departments, Admin. The application should evaluate the answers given by the students based on the feedback (which will be given by a no. 1 – 5) and grade has to be generated to all the staff members of a particular department. These feedback report was checked by the hods. He can view overall grades and view the grades obtained to the lecturers and give this report to the principal and he can give counseling to the college staff. “By using this online system we make it better and quick way.”

As market competition intensifies, customer churn management is increasingly becoming an important means of competitive advantage for companies. However, when dealing with big data in the industry, existing churn prediction models cannot work very well. In addition, decision makers are always faced with imprecise operations management. In response to these difficulties, a new clustering algorithm called Semantic Driven Subtractive Clustering Method (SDSCM) is proposed. Experimental results indicate that SDSCM has stronger clustering semantic strength than Subtractive Clustering Method (SCM) and fuzzy c-means (FCM). Then a parallel SDSCM algorithm is implemented through a Hadoop MapReduce framework. In the case study, the proposed parallel SDSCM algorithm enjoys a fast running speed when compared with the other methods. Furthermore, We provide some marketing strategies in accordance with the clustering results, and a simplified marketing activity is simulated to ensure profit maximization.

This project focuses on predicting the suspect of any crime by using various data and information stored on cloud database such as background of person involved in crimes, evidences collected from crime scenes. Based on the facts and data, our law system will punish the prime suspect. This system provides an easy way of finding the respective lawyers to both accused and victim, so that they will get proper judgements from our law. In other words, we can say we are developing a system which will provide an easy platform for investigating departments in finding the prime suspect who finds guilty in a particular crime.

Sequential rule mining is an important data mining problem with multiple applications. An important limitation of algorithms for mining sequential rules common to multiple sequences is that rules are very specific and therefore many similar rules may represent the same situation. This can cause three major problems: (1) similar rules can be rated quite differently, (2) rules may not be found because they are individually considered uninteresting, and (3) rules that are too specific are less likely to be used for making predictions. To address these issues, we explore the idea of mining “partially-ordered sequential  rules” (POSR), a more general form of sequential rules such that items in the antecedent and the consequent of each rule are unordered. To mine POSR, we propose the RuleGrowth algorithm, which is efficient and easily extendable. In particular, we present an extension (TRuleGrowth) that accepts a sliding-window constraint to find rules occurring within a maximum amount of time. A performance study with four real-life datasets show that RuleGrowth and TRuleGrowth have excellent performance and scalability compared to baseline algorithms and that the number of rules discovered can be several orders of magnitude smaller when the sliding-window constraint is applied. Furthermore, we also report results from a real application showing that POSR can provide a much higher prediction accuracy than regular sequential rules for sequence prediction.

Utility mining is a new development of data mining technology. Among utility mining problems, utility mining with the itemset share framework is a hard one as no anti-monotonicity property holds with the interestingness measure. Prior works on this problem all employ a two-phase, candidate generation approach with one exception that is however inefficient and not scalable with large databases. The two-phase approach suffers from scalability issue due to the huge number of candidates. This paper proposes a novel algorithm that finds high utility patterns in a single phase without generating candidates. The novelties lie in a high utility pattern growth approach, a lookahead strategy, and a linear data structure. Concretely, our pattern growth approach is to search a reverse set enumeration tree and to prune search space by utility upper bounding. We also look ahead to identify high utility patterns without enumeration by a closure property and a singleton property. Our linear data structure enables us to compute a tight bound for powerful pruning and to directly identify high utility patterns in an efficient and scalable way, which targets the root cause with prior algorithms. Extensive experiments on sparse and dense, synthetic and real world data suggest that our algorithm is up to 1 to 3 orders of  magnitude more efficient and is more scalable than the state-of-the-art algorithms.

Sentiment classification is a topic-sensitive task, i.e., a classifier trained from one topic will perform worse on another. This is especially a problem for the tweets sentiment analysis. Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. Moreover, compared to product review, Twitter lacks data labeling and a rating mechanism to acquire sentiment labels. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. In this paper, we propose a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics. It minimizes the hinge loss to adapt to unlabeled data and features including topic-related sentiment words, authors’ sentiments and sentiment connections derived from “@” mentions of tweets, named as topic-adaptive features. Text and non-text features are extracted and naturally split into two views for co-training. The TASC learning algorithm updates topic-adaptive features based on the collaborative selection of unlabeled data, which in turn helps to select more reliable tweets to boost the performance. We also design the adapting model along a timeline (TASC-t) for dynamic tweets. An experiment on 6 topics from published tweet corpuses demonstrates that TASC outperforms other well-known supervised and ensemble classifiers. It also beats those semi-supervised learning methods without feature adaption. Meanwhile, TASC-t can also achieve impressive accuracy and F-score. Finally, with timeline visualization of “river” graph, people can intuitively grasp the ups and downs of sentiments’ evolvement, and the intensity by color gradation.

The success of a movie is uncertain but it is no secret that it is dependent to a large extent upon the level of promotion and also upon the ratings received by the major movie critics. Time and money are valuable to the general audience and hence, they refer to the leading critics when making a decision about whether to watch a particular movie or not. Over 1000 movies on an average are produced per year. Therefore, in order to make the movie profitable, it becomes a matter of concern that the movie succeeds. Due to the low success rate, models and mechanisms to predict reliably the ranking and or box office collections of a movie can risk the business significantly. The current predictive models that are available are based on various factors for assessment of the movie. These include the typical factors such as cast, producer, director etc. or the social factors in form of response of the society on various online platforms. Various stakeholders such as actors, financiers, directors etc. can use these predictions to make more informed decisions.

A Theoretical Comparison of Job scheduling Algorithms in Cloud Computing Environment Cloud computing is a dynamic, scalable and payper-use distributed computing model empowering designers to convey applications amid job designation and storage distribution. Cloud computing encourages to impart a pool of virtualized computer resource empowering designers to convey applications amid job designation and storage distribution. The cloud computing mainly aims to give proficient access to remote and geographically distributed resources. As cloud technology is evolving day by day and confronts numerous challenges, one of them being uncovered is scheduling. Scheduling is basically a set of constructs constructed to have a controlling hand over the order of work to be performed by a computer system. Algorithms are vital to schedule the jobs for execution. Job scheduling algorithms is one of the most challenging hypothetical problems in the cloud computing domain area. Numerous deep investigations have been carried out in the domain of job scheduling of cloud computing. This paper intends to present the performance comparison analysis of various pre-existing job scheduling algorithms considering various parameters. This paper discusses about cloud computing and its constructs in section (i). In section (ii) job scheduling concept in cloud computing has been elaborated. In section (iii) existing algorithms for job scheduling are discussed, and are compared in a tabulated form with respect to various parameters and lastly section (iv) concludes the paper giving brief summary of the work.

It has become an era where everything is on the web with ever more chances of data utilization on the web. Still, there are obstacles to make the use of the web efficiently. With too much information, Internet users have often come across information that are not relevant for their use. On top of that, until recently, most of web content have not contained semantic information, posing difficulties for mechanical analysis. The Semantic Web emerged as a way to tackle those poor qualities of the web. Adopting formal languages such as RDF or OWL, the semantic web has made the Internet become more highly available for computer-based analysis. In this study, what we aimed at is building a small business knowledge base to provideuseful information for small business owners for their marketing strategies or dynamic QA systems for their restaurant recommendation services. The knowledge base was built according to the concept of the Semantic Web. To build the knowledge base, first, it is needed to conduct web crawling from different web sources including social media. However, the crawled data typically come in informal and do not have any semantic information. So we devised text mining techniques to catch useful information from them and generate formal knowledge for the knowledge base.

To recognize in vast restorative and variation groups with unstructured information is dependably a major test furthermore hazard component to exhibit outside world in an organized configuration. To overcome dependably the information ought to be stemmed and sorted in grouped parts. K-means is utilized as a part of the primary methodology of our as DDC. By taking the mean estimation of age and the groups will be encircled. Be that as it may, the groups are just mean based arrangement so we propose another methodology after K-Means based manufactured FFM. Taken after by that arbitrary markers set to get the achievable consequences of classification furthermore recurrence of appearance regarding allocated irregular scope of interesting qualities to every last existing mix tuple. To accomplish the wanted arrangement of activity we propose another methodology recognized bunching and attainable recurrence with extraordinary result for each procedure. These successions are trailed by non-standard pre-handling like self-cleaning of information and stemming. The pre-handling is finished by semi fluffy system. To accomplish these things of procedure we propose another calculation called DDC (unmistakable relocation for grouping) and UTOF (Unique recurrence result in expandable information. This procedure is absolutely on extensive therapeutic information which is constantly expandable with different new infections. The above calculation takes after a specific new system called doable fluffy mining (FFM) strategy

 Analysis of therapeutic information is very test in context of incremental development of properties and different parameters. Once the information is gathering there is specific farthest point to wind up in getting the information as tuples or ascertaining the recurrence of the combinational credits concerning age, ailment, sexual orientation. The most compelling motivation is to push the mysterious maladies in examination. In substantial information related information spotting of the fancied infection information is excessively muddled. So we utilize KNN with Euclidean separation component and choice tree with ordinary and upgraded model concerning given characteristics. In KNN Euclidean separation produced for all tuples and rank will be accommodate new characterization of new set and result created in light of k worth as closest neighbors. Be that as it may, for examination is between both above said as for investigation of time multifaceted nature for order. The principle characterization is done on tremendous and element patch information. So the fundamental arrangement is done on KNN methodology and immediate arrangement trees make up by utilizing NAE approach (typical and improved choice tree structures)

Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service.

Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry", "sad", and "happy".

A different method for determining sentiment is the use of a scaling system whereby words commonly associated with having a negative, neutral or positive sentiment with them are given an associated number on a -10 to +10 scale (most negative up to most positive). This makes it possible to adjust the sentiment of a given term relative to its environment (usually on the level of the sentence). When a piece of unstructured text is analyzed using natural language processing, each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score. This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it. Words, for example, that intensify, relax or negate the sentiment expressed by the concept can affect its score. Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text

</p>

<b> System Architecture </b> <br>

<img src = "http://www.ieeeprojectschennai.com/sites/default/files/images/content/Sentiment%20analysis%20.png">

</img>

<br>

<b> Project Overview </b>

After aquiring the twitter data and by taking the dictionaries(+ve and -ve) analisys will happen to prediction and +ve and -ve frequency for the recommendataions.

<br>

<b> <u> System Requirement </b> </u>

 <br>

<b> Hardware Requirement </b>

Processor                       -    Dual Core <br>

Speed                     -    1.1 G Hz   <br>

RAM                     -    512 MB (min)   <br>

Hard                      -    20 GB   <br>

Key Board            -    Standard Windows Keyboard   <br>

Mouse                  -     Two or Three Button Mouse   <br>

<b> Software Requirement </b>

<br>

Operating System          :         Windows xp,7,8  

Front End               :          Java 7

Technology          :         swings, core java

IDE                      :         Netbeans

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In recent years, it has been witnessed that the ever-interesting and upcoming publishing medium is the World Wide Web. Much of the web content is unstructured so gathering and making sense of such data is very tedious. Web servers worldwide generate a vast amount of information on web users’ browsing activities.Several researchers have studied these so-called web access log data to better understand and characterize web users. Data can be enriched with information about the content of visited pages and the origin (e.g., geographic, organizational) of the requests. The goal of this project is to analyze user   behavior by mining enriched web access log data. The several web usage mining methods for extracting useful features is discussed and employ all these techniques to cluster the users of the domain to study their behaviors comprehensively. The contributions of this thesis are a data enrichment that is content and origin based and a treelike visualization of frequent navigational sequences. This visualization allows for an easily interpretable tree-like view of patterns with highlighted relevant information. The results of this project can be applied on diverse purposes, including marketing, web content advising, (re- )structuring of web sites and several other E-business processes, like recommendation and advertiser systems. It also rank the best relevant documents based on Top K query for effective and efficient data retrieval system. It filters the web documents by providing the relevant content in the search engine result page (SERP).

Information extraction (IE) and knowledge discovery in databases (KDD) are both useful approaches for discovering information in textual corpora, but they have some deficiencies. Information extraction can identify relevant subsequences of text, but is usually unaware of emerging, previously unknown knowledge and regularities in a text and thus cannot form new facts or new hypotheses.

Complementary to information extraction, emerging data mining methods and techniques promise to overcome the deficiencies of information extraction. This research work combines the benefits of both approaches by integrating data mining and information extraction methods. The aim is to provide a new high-quality information extraction methodology and, at the same time, to improve the performance of the underlying extraction system. Consequently, the new methodology should shorten the life cycle of information extraction engineering because information predicted in early extraction phases can be used in further extraction steps, and the extraction rules developed require fewer arduous test-and-debug iterations. Effectiveness and applicability are validated by processing online documents from the areas of eHealth and eRecruitment.

Modern scientific databases and web databases maintain large and heterogeneous data. These real-world databases contain hundreds or even thousands of relations and attributes. Traditional predefined query forms are not able to satisfy various ad-hoc queries from users on those databases. This paper proposes DQF, a novel database query form interface, which is able to dynamically generate query forms. The essence of DQF is to capture a user’s preference and rank query form components, assisting him/her in making decisions. The generation of a query form is an iterative process and is guided by the user. At each iteration, the system automatically generates ranking lists of form components and the user then adds the desired form components into the query form. The ranking of form components is based on the captured user preference. A user can also fill the query form and submit queries to view the query result at each iteration. In this way, a query form could be dynamically refined until the user is satisfied with the query results. We utilize the expected F-measure for measuring the goodness of a query form. A probabilistic model is developed for estimating the goodness of a query form in DQF. Our experimental evaluation and user study demonstrate the effectiveness and efficiency of the system.

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate datasets will be generated, and often stored to save the cost of re-computing them. However, preserving the privacy of intermediate datasets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate datasets. Encrypting ALL datasets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate datasets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt datasets frequently while performing any operation on them. In this paper, we propose a novel upper-bound privacy leakage constraint based approach to identify which intermediate datasets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate datasets can be significantly reduced with our approach over existing ones where all datasets are encrypted.

In our proposed system is identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-todate with the latest discoveries. By using the tools such as NLP, ML techniques. In this research, focus on diseases and treatment information, and the relation that exists between these two entities. The main goal of this research is to identify the disease name with the symptoms specified and extract the sentence from the article and get the Relation that exists between Disease- Treatment and classify the information into cure, prevent, side effect to the user.This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a tree-based data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be generated efficiently with only two scans of database. The performance of UP-Growth and UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets. Experimental results show that the proposed algorithms, especially UPGrowth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime,especially when databases contain lots of long transactions.

Creating an efficient and economic trip plan is the most annoying job for a backpack traveler. Although travel agency can provide some predefined itineraries, they are not tailored for each specific customer. Previous efforts address the problem by providing an automatic itinerary planning service, which organizes the points-of-interests (POIs) into a customized itinerary. Because the search space of all possible itineraries is too costly to fully explore, to simplify the complexity, most work assume that user’s trip is limited to some important POIs and will complete within one day. To address the above limitation, in this paper, we design a more general itinerary planning service, which generates multiday itineraries for the users. In our service, all POIs are considered and ranked based on the users’ preference. The problem of searching the optimal itinerary is a team orienteering problem (TOP), a well-known NP-complete problem. To reduce the processing cost, a two-stage planning scheme is proposed. In its preprocessing stage, single-day itineraries are precomputed via the MapReduce jobs. In its online stage, an approximate search algorithm is used to combine the single day itineraries. In this way, we transfer the TOP problem with no polynomial approximation into another NP-complete problem (set-packing problem) with good approximate algorithms. Experiments on real data sets show that our approach can generate high-quality itineraries efficiently.

The vast majority of existing approaches to opinion feature extraction rely on mining patterns only from a single review corpus, ignoring the nontrivial disparities in word distributional characteristics of opinion features across different corpora. In this paper, we propose a novel method to identify opinion features from online reviews by exploiting the difference in opinion feature statistics across two corpora, one domain-specific corpus (i.e., the given review corpus) and one domain-independent corpus (i.e., the contrasting corpus). We capture this disparity via a measure called domain relevance (DR), which characterizes the relevance of a term to a text collection. We first extract a list of candidate opinion features from the domain review corpus by defining a set of syntactic dependence rules. For each extracted candidate feature, we then estimate its intrinsic-domain relevance (IDR) and extrinsic-domain relevance (EDR) scores on the domain-dependent and domain-independent corpora, respectively. Candidate features that are less generic (EDR score less than a threshold) and more domain-specific (IDR score greater than another threshold) are then confirmed as opinion features. We call this interval thresholding approach the intrinsic and extrinsic domain relevance (IEDR) criterion. Experimental results on two real-world review domains show the proposed IEDR approach to outperform several other well-established methods in identifying opinion features.

With the advancement in technology, industry, ecommerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing can resolve the problem of handling, storage and analyzing the Big Data as it distributes the big data within the cloudlets. No doubt, Cloud Computing is the best answer available to the problem of Big Data storage and its analyses but having said that, there is always a potential risk to the security of Big Data storage in Cloud Computing, which needs to be addressed. Data Privacy is one of the major issues while storing the Big Data in a Cloud environment. Data Mining based attacks, a major threat to the data, allows an adversary or an unauthorized user to infer valuable and sensitive information by analyzing the results
generated from computation performed on the raw data. This thesis proposes a secure k-means data mining approach assuming the data to be distributed among different hosts preserving the privacy of the data. The approach is able to maintain the correctness and validity of the existing k-means to generate the final results even in the distributed environment.

Keyword search is an intuitive paradigm for searching linked data sources on the web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multilevel scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets, and subgraphs that connect these elements. Experiments carried out using 150 publicly available sources on the web showed that valid plans (precision@1 of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be computed in 1 second on average on a single PC. Further, we show routing greatly helps to improve the performance of keyword search, without compromising its result quality.

Mining sequential rules from large databases is an important topic in data mining fields with wide applications. Most of the relevant studies focused on finding sequential rules appearing in a single sequence of events and the mining task dealing with multiple sequences were far less explored. In this paper, we present RuleGrowth, a novel algorithm for mining sequential rules common to several sequences. Unlike other algorithms, RuleGrowth uses a pattern-growth approach for discovering sequential rules such that it can be much more efficient and scalable. We present a comparison of RuleGrowth’s performance with current algorithms for three public datasets. The experimental results show that RuleGrowth clearly outperforms current algorithms for all three datasets under low support and confidence threshold and has a much better scalability.

Now a day's Data mining has a lot of e-Commerce applications. The key problem is how to find useful hidden
patterns for better business applications in the retail sector. For the solution of these problems, The Apriori algorithm is one of the most popular data mining approach for finding frequent item sets from a transaction dataset and derive association rules. Rules are the discovered knowledge from the data base. Finding frequent item set (item sets with frequency larger than or equal to a user specified minimum support) is not trivial because of its combinatorial explosion. Once frequent item sets are obtained, it is straightforward to generate association rules with confidence larger than or equal to a user specified minimum confidence. The paper illustrating apriori algorithm on simulated database and finds the association rules on different confidence value.

We propose a protocol for secure mining of association rules in horizontally distributed databases. The current leading protocol is that of Kantarcioglu and Clifton [18]. Our protocol, like theirs, is based on the Fast Distributed Mining (FDM) algorithm of Cheung et al. [8], which is an unsecured distributed version of the Apriori algorithm. The main ingredients in our protocol are two novel secure multi-party algorithms—one that computes the union of private subsets that each of the interacting players hold, and another that tests the inclusion of an element held by one player in a subset held by another. Our protocol offers enhanced privacy with respect to the protocol in [18]. In addition, it is simpler and is significantly more efficient in terms of communication rounds, communication cost and computational cost

This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ) :B, :A) B, and :A ) :B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency. Categories and Subject Descriptors: I.2.6

This paper proposes a fault node recovery algorithm to enhance the lifetime of a wireless sensor network when some of the sensor nodes shut down. The algorithm is based on the grade diffusion algorithm combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes up to 8.7 times, reduces the rate of data loss by approximately 98.8%, and reduces the rate of energy consumption by approximately 31.1%.

Wireless Sensor Network (WSN) is used for collecting the information from the environment. WSN consists of large number of Sensor Nodes (SN). To implement security during the transmission of data from one node to another node, different security techniques are used. Authentication is an essential requirement in sensor network pursuing security. But the Wireless Sensor Networks are very difficult to secure due to its dynamic and ad-hoc nature. To analyze the security issues that arise try to solve by integrating Wireless Sensor Networks (WSN) with the mobile network and can utilize the combined capabilities of both networks. To address the problem of authentication in WSNs this paper presents propose an efficient and secure framework which provide authentication to a roaming sensor node while allowing a sensor node to move across multiple WSNs. The proposed key management technique to provide authentication is by using Virtual Certificate Authority (VCA) which is designed especially for distributed Adhoc network

Service-oriented architectures for wireless sensor networks (WSNs) have been proposed to provide an integrated platform, where new applications can be rapidly developed through flexible service composition. In WSNs, the existing multipath routing schemes have demonstrated the effectiveness of traffic distribution over multipaths to fulfill the quality of service requirements of applications. However, the failure of links might significantly affect the transmission performance, scalability, reliability, and security of WSNs. Thus, by considering the reliability, congestion control, and security for multipath, it is desirable to design a reliable and service-driven routing scheme to provide efficient and failure-tolerant routing scheme. In this paper, an evaluation metric, path vacant ratio, is proposed to evaluate andthen find a set of link-disjoint paths from all available paths. A congestion control and load-balancing algorithm that can adaptively adjust the load over multipaths is proposed. A threshold sharing algorithm is applied to split the packets into multiple segments that will be delivered via multipaths to the destination depending on the path vacant ratio. Simulations demonstrate the performance of the adaptive and secure load-balance routing scheme.

In this paper, we consider the problem of link scheduling in multihop wireless networks under general interference constraints. Our goal is to design scheduling schemes that do not use per-flow or per-destination information, maintain a single data queue for each link, and exploit only local information, while guaranteeing throughput optimality. Although the celebrated back-pressure algorithm maximizes throughput, it requires per-flow or per-destination information. It is usually difficult to obtain and maintain this type of information, especially in large networks, where there are numerous flows. Also, the back-pressure algorithm maintains a complex data structure at each node, keeps exchanging queue-length information among neighboring nodes, and commonly results in poor delay performance. In this paper, we propose scheduling schemes that can circumvent these drawbacks and guarantee throughput optimality. These schemes use either the readily available hop-count information or only the local information for each link. We rigorously analyze the performance of the proposed schemes using fluid limit techniques via an inductive argument and show that they are throughput-optimal. We also conduct simulations to validate our theoretical results in various settings and show that the proposed schemes can substantially improve the delay performance in most scenarios

The resource efficiency and dependability of a trust system are the most fundamental requirements for any wireless sensor network (WSN). However, existing trust systems developed for WSNs are incapable of satisfying these requirements because of their high overhead and low dependability. In this work, we proposed a lightweight and dependable trust system (LDTS) for WSNs, which employ clustering algorithms. First, a lightweight trust decision-making scheme is proposed based on the nodes' identities (roles) in the clustered WSNs, which is suitable for such WSNs because it facilitates energy-saving. Due to canceling feedback between cluster members (CMs) or between cluster heads (CHs), this approach can significantly improve system efficiency while reducing the effect of malicious nodes. More importantly, considering that CHs take on large amounts of data forwarding and communication tasks, a dependability-enhanced trust evaluating approach is defined for cooperations between CHs. This approach can effectively reduce networking consumption while malicious, selfish, and faulty CHs. Moreover, a self-adaptive weighted method is defined for trust aggregation at CH level. This approach surpasses the limitations of traditional weighting methods for trust factors, in which weights are assigned subjectively. Theory as well as simulation results shows that LDTS demands less memory and communication overhead compared with the current typical trust systems for WSNs.

The Open Nature of wireless medium leaves an intentional interference attack, typically referred to as jamming. This intentional interference with wireless transmission launch pad for mounting Denial-Of- Service attack on wireless networks. Typically, jamming has been addresses under an external threat model. However, adversaries with internal knowledge of protocol specification and network secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this work we address the problem of jamming attacks and adversary is active for short period of time, selectively targeting the messages of high importance. We show that the selective jamming attacks can be launched by performing real-time packet classification at the physical layer. To mitigate these attacks, we develop three schemes that prevent realtime packet classification by combining cryptographic primitives with physical-layer attributes. They are Strong Hiding Commitment Schemes (SHCS), Cryptographic Puzzles Hiding Schemes (CPHS), All- Or-Nothing Transformation Hiding Schemes (AONTSHS). Random key distribution methods are done along with three schemes to give more secured packet transmission in wireless networks.

The Open Nature of wireless medium leaves an intentional interference attack, typically referred to as jamming. This intentional interference with wireless transmission launch pad for mounting Denial-Of- Service attack on wireless networks. Typically, jamming has been addresses under an external threat model. However, adversaries with internal knowledge of protocol specification and network secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this work we address the problem of jamming attacks and adversary is active for short period of time, selectively targeting the messages of high importance. We show that the selective jamming attacks can be launched by performing real-time packet classification at the physical layer. To mitigate these attacks, we develop three schemes that prevent realtime packet classification by combining cryptographic primitives with physical-layer attributes. They are Strong Hiding Commitment Schemes (SHCS), Cryptographic Puzzles Hiding Schemes (CPHS), All- Or-Nothing Transformation Hiding Schemes (AONTSHS). Random key distribution methods are done along with three schemes to give more secured packet transmission in wireless networks.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non-repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol and apply our framework to the important data leakage scenarios of data outsourcing and social networks. In general, we consider LIME , our lineage framework for data transfer, to be an key step towards achieving accountability by design

Mobile adhoc network (MANET)is a type of wireless network. It is a open network environment, in other words it is a infrastructreless network. So any node can join and leave in the network freely. Security is the primary concern in the wireless medium. Major functionality of MANET is protecting the data bits which transfer from one node to another node. Because of its dynamic nature MANET is not much secure than wired network. To overcome this problem, cluster based certificate revocation with vindication capability is proposed. In this paper we present the cluster based architecture is used to construct the topology and provide better security for data transfer in MANET

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead MANETs suffering from many security vulnerabilities. In this paper, using recent advances
in uncertain reasoning originated from artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory, which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. Combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio can be improved significantly with slightly increased average endto- end delay and overhead of messages.

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead MANETs suffering from many security vulnerabilities. In this paper, using recent advances
in uncertain reasoning originated from artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory, which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. Combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio can be improved significantly with slightly increased average endto- end delay and overhead of messages.

Mobile Ad-hoc Network is highly susceptible towards the security attacks due to its dynamic topology, resource constraint, energy constraint operations, limited physical security and lack of infrastructure. Misleading routing attack (MIRA) in MANET intend to delay packet to its fullest in order to generate time outs at the source as packets will not reach in time. Its main objective is to generate delay and increase network overhead. It is a variation to the sinkhole attack. In this paper, we have proposed a detection scheme to detect the malicious nodes at route discovery as well as at packet transmissions. The simulation results of MIRA attack indicate that though delay is increased by 91.30% but throughput is not affected which indicates that misleading routing attack is difficult to detect. The proposed detection scheme when applied to misleading routing attack suggests a significant decrease in delay.

With rapid development of cloud computing, the need for an architecture to follow in developing cloud computing applications is necessary. Existing architectures lack the way cloud applications are developed. They focus on clouds' structure and how to use clouds as a tool in developing cloud computing applications rather than focusing on how applications themselves are developed using clouds. This paper presents a survey on key cloud computing concepts, definitions, characteristics, development phases, and architectures. Also, it proposes and describes a novel architecture, which aid developers to develop cloud computing applications in a systematic way. It discusses how cloud computing transforms the way applications are developed/delivered and describes the architectural considerations that developers must take when adopting and using cloud computing technology.

Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructure. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software failures. Dependability assurance is crucial for building sustainable cloud computing services. Although many techniques have been proposed to analyze and enhance reliability of distributed systems, there is little work on understanding the dependability of cloud computing environments. As virtualization has been an enabling technology for the cloud, it is imperative to investigate the impact of virtualization on the cloud dependability, which is the focus of this work. In this paper, we present a cloud dependability analysis (CDA) framework with mechanisms to characterize failure behavior in cloud computing infrastructures. We design the failure-metric DAGs (directed a cyclic graph) to analyze the correlation of various performance metrics with failure events in virtualized and non-virtualized systems. We study multiple types of failures. By comparing the generated DAGs in the two environments, we gain insight into the impact of virtualization on the cloud dependability. This paper is the first attempt to study this crucial issue. In addition, we exploit the identified metrics for failure detection. Experimental results from an on-campus cloud computing test bed show that our approach can achieve high detection accuracy while using a small number of performance metrics.

Personal health record (PHR) is an emerging patient-centric model of health information exchange, which is often outsourced to be stored at a third party, such as cloud providers. However, there have been wide privacy concerns as personal health information could be exposed to those third party servers and to unauthorized parties. To assure the patients’ control over access to their own PHRs, it is a promising method to encrypt the PHRs before outsourcing. Yet, issues such as risks of privacy exposure, scalability in key management, flexible access and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. In this paper, we propose a novel patient-centric framework and a suite of mechanisms for data access control to PHRs stored in semi-trusted servers. To achieve fine-grained and scalable data access control for PHRs, we leverage attribute based encryption (ABE) techniques to encrypt each patient’s PHR file. Different from previous works in secure data outsourcing, we focus on the multiple data owner scenario, and divide the users in the PHR system into multiple security domains that greatly reduces the key management complexity for owners and users. A high degree of patient privacy is guaranteed simultaneously by exploiting multi-authority ABE. Our scheme also enables dynamic modification of access policies or file attributes, supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results are presented which show the security, scalability and efficiency of our proposed scheme

Cloud computing has been envisioned as the de-facto solution to the rising storage costs of IT Enterprises. With the high costs of data storage devices as well as the rapid rate at which data is being generated it proves costly for enterprises or individual users to frequently update their hardware. Apart from reduction in storage costs data outsourcing to the cloud also helps in reducing the maintenance. Cloud storage moves the user’s data to large data centers, which are remotely located, on which user does not have any control. However, this unique feature of the cloud poses many new security challenges which need to be clearly understood and resolved. We provide a scheme which gives a proof of data integrity in the cloud which the customer can employ to check the correctness of his data in the cloud. This proof can be agreed upon by both the cloud and the customer and can be incorporated in the Service Level Agreement (SLA).

Despite the surge in Vehicular Ad Hoc NETwork (VANET) research, future high-end vehicles are expected to under-utilize the on-board computation, communication, and storage resources. Olariu et al. envisioned the next paradigm shift from conventional VANET to Vehicular Cloud Computing (VCC) by merging VANET with cloud computing. But to date, in the literature, there is no solid architecture for cloud computing from VANET standpoint. In this paper, we put forth the taxonomy of VANET based cloud computing. It is, to the best of our knowledge, the first effort to define VANET Cloud architecture. Additionally we divide VANET clouds into three architectural frameworks named Vehicular Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Vehicular Clouds (HVC). We also outline the unique security and privacy issues and research challenges in VANET clouds.

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

Although the cloud computing model is considered to be a very promising internet-based computing platform, it results in a loss of security control over the cloud-hosted assets. This is due to the outsourcing of enterprise IT assets hosted on third-party cloud computing platforms. Moreover, the lack of security constraints in the Service Level Agreements between the cloud providers and consumers results in a loss of trust as well. Obtaining a security certificate such as ISO 27000 or NIST-FISMA would help cloud providers improve consumers trust in their cloud platforms' security. However, such standards are still far from covering the full complexity of the cloud computing model. We introduce a new cloud security management framework based on aligning the FISMA standard to fit with the cloud computing model, enabling cloud providers and consumers to be security certified. Our framework is based on improving collaboration between cloud providers, service providers and service consumers in managing the security of the cloud platform and the hosted services. It is built on top of a number of security standards that assist in automating the security management process. We have developed a proof of concept of our framework using. NET and deployed it on a test bed cloud platform. We evaluated the framework by managing the security of a multi-tenant SaaS application exemplar.

Electronic health is vital for enabling improved access to health records, and boosting the quality of the health services provided. In this paper, a framework for an electronic health record system is to be developed for connecting a nation's health care facilities together in a network using cloud computing technology. Cloud computing ensures easy access to health records from anywhere and at any time with easy scalability and prompt on demand availability of resources. A hybrid cloud is to adopted in modeling the system and solutions are proposed for the main challenges faced in any typical electronic health record system.

The advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in cloud, it is crucial for the search service to allow multi-keyword query and provide result similarity ranking to meet the effective data retrieval need. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely differentiate the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of “coordinate matching”, i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use “inner product similarity” to quantitatively formalize such principle for similarity measurement. We first propose a basic MRSE scheme using secure inner product computation, and then significantly improve it to meet different privacy requirements in two levels of threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given, and experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.

Cloud computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user. Specifically,our contribution in this work can be summarized as the following three aspects:
1) We motivate the public auditing system of data storage security in Cloud Computing and provide a privacy-preserving auditing protocol, i.e., our scheme supports an external auditor to audit user’s outsourced data in the cloud without learning knowledge on the data content.
2) To the best of our knowledge, our scheme is the first to support scalable and efficient public auditing in the Cloud Computing. In particular, our scheme achieves batch auditing where multiple delegated auditing tasks from different users can be performed simultaneously by the TPA.
3) We prove the security and justify the performance of our proposed schemes through concrete experiments and comparisons with the state-of-the-art.

Cloud computing is fundamentally altering expectations for how and when computing, storage an networking resources should be allocated, managed and consumed. End-users are increasingly sensitive to the latency of services they consume. Service Developers want the Service Providers to ensure or provide the capability to dynamically allocate and manage resources in response to changing demand patterns in real-time. Ultimately, Service Providers are under pressure to architect their infrastructure to enable real-time end-to-end visibility and dynamic resource management with fine grained control to reduce total cost of ownership while also improving agility. The current approaches to enabling real-time, dynamic infrastructure are inadequate, expensive and not scalable to support consumer mass-market requirements. Over time, the server-centric infrastructure management systems have evolved to become a complex tangle of layered systems designed to automate systems administration functions that are knowledge and labor intensive. This expensive and non-real time paradigm is ill suited for a world where customers are demanding communication, collaboration and commerce at the speed of light. Thanks to hardware assisted virtualization, and the resulting decoupling of infrastructure and application management, it is now possible to provide dynamic visibility and control of service management to meet the rapidly growing demand for cloud-based services.

A new method on matrix scrambling based on the elliptic curve will be proposed in this paper. The proposed algorithm is based on random function and shifting technique of circular queue. In particular, we first transform the message into points on the elliptic curve as is the embedding system M _PM and then apply the encryption/decryption technique based on matrix scrambling. Our scheme is secure against most of the current attacking mechanisms.

In this paper, we propose a distributed key management framework based on group signature to provision privacy in vehicular ad hoc networks (VANETs). Distributed key management is expected to facilitate the revocation of malicious vehicles, maintenance of the system, and heterogeneous security policies, compared with the centralized key management assumed by the existing group signature schemes. In our framework, each road side unit (RSU) acts as the key distributor for the group, where a new issue incurred is that the semi-trust RSUs may be compromised. Thus, we develop security protocols for the scheme which are able to detect compromised RSUs and their colluding malicious vehicles. Moreover, we address the issue of large computation overhead due to the group signature implementation. A practical cooperative message authentication protocol is thus proposed to alleviate the verification burden, where each vehicle just needs to verify a small amount of messages. Details of possible attacks and the corresponding solutions are discussed. We further develop a medium access control (MAC) layer analytical model and carry out NS2 simulations to examine the key distribution delay and missed detection ratio of malicious messages, with the proposed key management framework being implemented over 802.11 based VANETs.

The rapid growth of the computers that are interconnected, the crime rate has also increased and the ways to mitigate those crimes has become the important problem now. In the entire globe, organizations, higher learning institutions and governments are completely dependent on the computer networks which plays a major role in their daily operations. Hence the necessity for protecting those networked systems has also increased. Cyber crimes like compromised server, phishing and sabotage of privacy information has increased in the recent past. It need not be a massive intrusion, instead a single intrusion can result in loss of highly privileged and important data. Intusion behaviour can be classified based on different attack types. Smart intruders will not attack using a single attack, instead, they will perform the attack by combining few different attack types to deceive the detection system at the gateway. As a countermeasure, computational intelligence can be applied to the intrusion detection systems to realize the attacks, alert the administrator about the form and severity, and also to take any predetermined or adaptive measures dissuade the intrusion.

There are many solution based methods created against distributed denial of service (DDoS) attacks are focused on the Transmission Control Protocol and Internet Protocol layers as a substitute of the high layer. The DDoS attack makes an attempt to make a computer resource unavailable to its respective users. DoS attacks are implemented by either forcing the targeted computer(s) to reset, or consuming its resources so that it can no longer provide its intended service and actually they are not suitable for handling the new type of attack which is based on the application layer. In this project, we establish a new system to achieve early attack discovery and filtering for the application-layer-based DDoS attack. An extended hidden semi-Markov model is proposed to describe the browsing habits of web searchers. A forward algorithm is derived for the online implementation of the model based on the M-algorithm in order to reduce the computational amount introduced by the model’s large state space. Entropy of the user’s HTTP request sequence accurate to the replica is used as a principle to measure the user’s normality. Finally, experiments are conducted to validate our model and algorithm.

Online exam is field that is very popular and made many security assurances. Then also it fails to control cheating. Online exams have not been widely adopted well, but online education is adopted and using allover the world without any security issues. An online exam is defined in this project as one that takes place over the insecure Internet, and where no proctor is in the same location as the examinees. This project proposes an enhanced secure filled online exam management environment mediated by group cryptography techniques using remote monitoring and control of ports and input. The target domain of this project is that of online exams for any subject’s contests in any level of study, as well as exams in online university courses with students in various remote locations. This project proposes a easy solution to the issue of security and cheating for online exams. This solution uses an enhanced Security Control system in the Online Exam (SeCOnE) which is based on group cryptography.

Now a day the usage of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. In this Project, we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multi-source networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network's ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios.

A data distributor has given sensitive data to a set of supposedly trusted agents (third parties). Some of the data are leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We propose data allocation strategies (across the agents) that improve the probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and identifying the guilty party.

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs.

Maximizing the lifetime of a sensor network has been a subject of intensive study. However, much prior work defines the network lifetime as the time before the first data-generating sensor in the network runs out of energy or is not reachable to the sink due to network partition. The problem is that even though one sensor is out of operation, the rest of the network may well remain operational, with other sensors generating useful data and delivering those data to the sink. Hence, instead of just maximizing the time before the first sensor is out of operation, we should maximize the lifetime vector of the network, consisting of the lifetimes of all sensors, sorted in ascending order. For this problem, there exists only a centralized algorithm that solves a series of linear programming problems with high-order complexities. This paper proposes a fully distributed algorithm that runs iteratively. Each iteration produces a lifetime vector that is better than the vector produced by the previous iteration. Instead of giving the optimal result in one shot after lengthy computation, the proposed distributed algorithm has a result at any time, and the more time spent gives the better result. We show that when the algorithm stabilizes, its result produces the maximum lifetime vector. Furthermore, simulations demonstrate that the algorithm is able to converge rapidly toward the maximum lifetime vector with low overhead.

Wireless sensor networks (WSNs) have attracted a lot of researchers due to their usage in critical applications. WSN have limitations on computational capacity, battery etc which provides scope for challenging problems. Applications of WSN are drastically growing from indoor deployment to critical outdoor deployment. WSN are distributed and deployed in an un attend environment, due to this WSN are vulnerable to numerous security threats. The results are not completely trustable due to their deployment in outside and uncontrolled environments. In this current paper, we fundamentally focused on the security issue of WSNs and proposed a protocol based on public key cryptography for external agent authentication and session key establishment. The proposed protocol is efficient and secure in compared to other public key based protocols in WSNs.

Wireless Sensor Networks (WSNs) are key for various applications that involve long-term and low-cost monitoring and actuating. In these applications, sensor nodes use batteries as the sole energy source. Therefore, energy efficiency becomes critical. We observe that many WSN applications require redundant sensor nodes to achieve fault tolerance and Quality of Service (QoS) of the sensing. However, the same redundancy may not be necessary for multihop communication because of the light traffic load and the stable wireless links. In this paper, we present a novel sleep-scheduling technique called Virtual Backbone Scheduling (VBS). VBS is designed for WSNs has redundant sensor nodes. VBS forms multiple overlapped backbones which work alternatively to prolong the network lifetime. In VBS, traffic is only forwarded by backbone sensor nodes, and the rest of the sensor nodes turn off their radios to save energy. The rotation of multiple backbones makes sure that the energy consumption of all sensor nodes is balanced, which fully utilizes the energy and achieves a longer network lifetime compared to the existing techniques. The scheduling problem of VBS is formulated as the Maximum Lifetime Backbone Scheduling (MLBS) problem. Since the MLBS problem is NP-hard, we propose approximation algorithms based on the Schedule Transition Graph (STG) and Virtual Scheduling Graph (VSG). We also present an Iterative Local Replacement (ILR) scheme as a distributed implementation. Theoretical analyses and simulation studies verify that VBS is superior to the existing techniques.

A wireless sensor network is the network consisting of numerous small sensor nodes with sensing, processing and wireless communication capabilities. Many routing protocols are suggested for wireless sensor networks due to the several limited resources of a sensor node such as its limited CPU, memory size, and battery. They can be divided into flat and hierarchical routing protocols. The hierarchical routing protocol uses the clustering scheme and shows better performance than flat routing protocols. However, there is an assumption that sensor nodes can communicate with the base station by a one-hop routing in the hierarchical routing protocol. However, if the network size become larger, the hierarchical routing protocol is unsuitable because a long distance between a clusterhead and the base station can cause some communication problems. In this paper, we propose the clusterhead chaining scheme to solve this problem. Our scheme is suitable for vast wireless sensor networks and it was found from the simulation result that the proposed scheme shows better performance than the general hierarchical routing protocol.

Due to the unattended nature of wireless sensor networks, an adversary can capture and compromise sensor nodes, generate replicas of those nodes, and mount a variety of attacks with the replicas he injects into the network. These attacks are dangerous because they allow the attacker to leverage the compromise of a few nodes to exert control over much of the network. Several replica node detection schemes in the literature have been proposed to defend against these attacks in static sensor networks. These approaches rely on fixed sensor locations and hence do not work in mobile sensor networks, where sensors are expected to move. In this work, we propose a fast and ¬¬effective mobile replica node detection scheme using the Sequential Probability Ratio Test. To the best of our knowledge, this is the first work to tackle the problem of replica node attacks in mobile sensor networks. We show analytically and through simulation experiments that our schemes achieve effective and robust replica detection capability with overheads.

Most explanations of the user behavior while interacting with the web are based on a top-down approach, where the entire Web, viewed as a vast collection of pages and interconnection links, is used to predict how the users interact with it. A prominent example of this approach is the random-surfer model, the core ingredient behind Google’s PageRank. This model exploits the linking structure of the Web to estimate the percentage of web surfers viewing any given page. Contrary to the top-down approach, a bottom-up approach starts from the user and incrementally builds the dynamics of the web as the result of the users’ interaction with it. The second approach has not being widely investigated, although there are numerous advantages over the top-down approach regarding (at least) personalization and decentralization of the required infrastructure for web tools. In this paper, we propose a bottom-up approach to study the web dynamics based on web-related data browsed, collected, tagged, and semi-organized by end users. Our approach has been materialized into a hybrid bottom-up search engine that produces search results based solely on user provided web-related data and their sharing among users. We conduct an extensive experimental study to demonstrate the qualitative and quantitative characteristics of user generated web-related data, their strength, and weaknesses as well as to compare the search results of our bottom-up search engine with those of a traditional one. Our study shows that a bottom-up search engine starts from a core consisting of the most interesting part of the Web (according to user opinions) and incrementally (and measurably) improves its ranking, coverage, and accuracy. Finally, we discuss how our approach can be integrated with PageRank, resulting in a new page ranking algorithm that can uniquely combine link analysis with users’ preferences.

Web-based collaborations have become essential in today’s business environments. Due to the availability of various SOA frameworks, Web services emerged as the de facto technology to realize flexible compositions of services. While most existing work focuses on the discovery and composition of software based services, we highlight concepts for a people-centric Web. Knowledge-intensive environments clearly demand for provisioning of human expertise along with sharing of computing resources or business data through software-based services. To address these challenges, we introduce an adaptive approach allowing humans to provide their expertise through services using SOA standards, such as WSDL and SOAP. The seamless integration of humans in the SOA loop triggers numerous social implications, such as evolving expertise and drifting interests of human service providers. Here we propose a framework that is based on interaction monitoring techniques enabling adaptations in SOA-based socio-technical systems.

Decision making and understanding the behavior of the customer has become vital and challenging problem for organizations to sustain their position in the competitive markets. Technological innovations have paved breakthrough in faster processing of queries and sub-second response time. Data mining tools have become surest weapon for analyzing huge amount of data and breakthrough in making correct decisions. The objective of this paper is to analyze the huge amount of data thereby exploiting the consumer behavior and make the correct decision leading to competitive edge over rivals. Experimental analysis has been done employing association rules using Market Basket Analysis to prove its worth over the conventional methodologies.

Android platform is a new generation of smart mobile phone platform launched by Google. Android provides the support of location service. So far, the development of location applications is complex and difficult. This paper introduces the architecture and component models of Android, and analyzes the anatomy of an Android application including the functions of Activity, Intent Receiver, Service, SMS, and etc. Based on Android, the design method of a location-based mobile service is then presented. The design example shows that it's so easy to implement location application which fetches latitude and longitudinal values and sends through the desired phone number.

Android is a software stack for mobile devices that includes an operating system, middleware and key applications. The Android SDK provides the tools and APIs necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of us serving multiple needs of humans. This application makes use of the Bluetooth feature of mobile phone as a solution for industrial automation. It comes handy for the employs working in industry for their basic necessities. Silent Features of the application • Turn ON / OFF LIGHT • Turn ON / OFF FAN • Turn ON / OFF A/C You can control all these by just running an application in your Android based phone. Bluetooth is the mode of communication between the hardware and you.

In current tourism system, whenever a tourist visits famous spots, to know more about the place he hires a guide. The hired guide then narrates history of the place. The proposed system doesn’t require a physical guide. The Mobile application installed on the mobile of tourist can act as a guide. Additionally, the application would help user to find out the weather forecast of the place.

In current system, alarms are set for particular time. Many times there are situations where the alarm/reminder is based on your current location nad not based on time. The Mobile application installed on the mobile can give a alarm based on a particular location. Additionally, the application would help user to find out how far the user is away from particular reminder location on the map.

Due to the heterogeneity involved in smart interconnected devices, cellular applications, and surrounding (GPS-aware) environments there is a need to develop a realistic approach to track mobile assets. Current tracking systems are costly and inefficient over wireless data transmission systems where cost is based on the rate of data being sent. Our aim is to develop an efficient and improved geographical asset tracking solution and conserve valuable mobile resources by dynamically adapting the tracking scheme by means of context-aware personalized route learning techniques. We intend to perform this tracking by proactively monitoring the context information in a distributed, efficient, and scalable fashion. Context profiles, which indicate the characteristics of a route based on environmental conditions, are utilized to dynamically represent the values of the asset's properties. We designed and implemented an adaptive learning based scheme that makes an optimized judgment of data transmission. This manuscript is complemented with theoretical and practical evaluations that prove that significant costs can be saved and operational efficiency can be achieved.

Android is a software stack for mobile devices that includes an operating system, middleware and key applications. The Android SDK provides the tools and APIs necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of us serving multiple needs of humans. In this project we developed a proof-of-concept system that addressed traffic safety in school zones. Our system addresses the need for drivers to be able to pay full visual attention to the road while still being alerted to the speed of the car. The system integrated several of the features that are becoming more commonplace on smartphones as well as information retrieved from Internet services.

Blue-tooth chat applications can be seen as an alternative and effective way of communicating for people without the need of using the mobile telephony system. Based on the new generation of cellular phones with support for communication technologies, such as Blue-tooth and WI-Fi, it is possible to develop applications to enable Blue-tooth chats. Such applications can provide mechanisms to discover and communicate with other devices in a shorter range, but with low or no communication costs Blue-tooth chat is a direct text chat between two or more users, where every participant uses a Bluetooth device (i.e. a modern mobile phone or a PDA) and names it (it will be the user's nickname). The device is generally used in a public and populated space (like a pub, a street, plaza and so on).

This application makes use of the Bluetooth & GPS features of mobile phone as a solution for Vehicle theft control system. This paper deals with the design & development of a theft control system for an automobile, which is being used to prevent/control the theft of a vehicle. The developed system makes use of an embedded system based on GSM technology. The designed & developed system is installed in the vehicle. An interfacing mobile is also connected to the microcontroller, which is in turn, connected to the engine. Once, the vehicle is being stolen, the information is being used by the vehicle owner for further processing. The information is passed onto the central processing insurance system, where by sitting at a remote place, a particular number is dialed by them to the interfacing mobile that is with the hardware kit which is installed in the vehicle. By reading the signals received by the mobile, one can control the ignition of the engine; say to lock it or to stop the engine immediately. Again it will come to the normal condition only after entering a secured password. The owner of the vehicle & the central processing system will know this secured password. The main concept in this design is introducing the mobile communications into the embedded system. The designed unit is very simple & low cost.

This application makes use of the Bluetooth and GPS feature of mobile phone as a solution for irrigation control system. In our project we are going to implement the irrigation control system for farmers for efficient use of water, power and crop planning by using GSM and GPS technology.

Out-patient medication administration has been identified as the most error prone procedure amidst the entire medication process. Most of these errors were made when patients bought different prescribed and over-the-scounter (OTC) medicines from several drug stores and use them at home without little or no guidance.

Android is a software stack for mobile devices that includes an operating system, middleware and key applications. The Android SDK provides the tools and APIs necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of us serving multiple needs of humans. This application makes use of the Bluetooth & GPS features of mobile phone as a solution for Child tracking. In our project we are going to implement child security and the safety. Child Tracking system is a combination of mobile and server side application to provide enhanced child security by locating a child in real time. Each child is provided with a mobile device with the tracking application that sends data to the server asynchronously or on demand. Most of the parents given their children a mobile handset to be constant touch with them. They were worried to call their children every time. Looking at this worry of this parents we are going to create a tracking Application.

Mobile devices need more processing power but energy consumption should be less to save battery power. Open Handset Alliance (OHA) hosting members like Google, Motorola, HTC etc released an open source platform Android for mobile devices. Android runs on top of Linux kernel with a custom JVM set on top of it. In this work we try to make Android as energy efficient as possible to save battery power in mobile devices. Especially the blue-tooth, Wifi, Gps are the main concerns that drains the battery power. It manages the battery level in mobile. Initially we get the battery information from the mobile like the current battery percentage, battery health, and voltage and temperature .And in next part we set a threshold value for the battery level when it gets lower the threshold value the screen gets dim ,Blue-tooth and Wifi and Gps gets turned off.

This application makes use of the Bluetooth and GPS feature of mobile phone as a solution for remote billing and control system. It is the technology used to determine the location of a Asset using different methods like GPS and other radio navigation systems operating through satellites and ground based stations. This system is an important tool for tracking each Asset at a given period of time and now it is becoming increasingly popular for people having expensive cars and hence as a theft prevention and retrieval device. No doubt, Asset tracking system whether it is GPS based or any other wireless medium has brought one of the most important technological advances in today’s communication field.

Android is a software stack for mobile devices that includes an operating system, middleware and key applications. The Android SDK provides the tools and APIs necessary to begin developing applications on the Android platform using the Java programming language. Mobile phones have almost become an integral part of us serving multiple needs of humans. This application makes use of the Bluetooth & GPS features of mobile phone as a solution for Child tracking. In our project we are going to implement child security and the safety. Child Tracking system is a combination of mobile and server side application to provide enhanced child security by locating a child in real time. Each child is provided with a mobile device with the tracking application that sends data to the server asynchronously or on demand. Most of the parents given their children a mobile handset to be constant touch with them. They were worried to call their children every time. Looking at this worry of this parents we are going to create a tracking Application.

In current system, in order to find out the location of friends, user need to call and ask friend about his where abouts. The proposed system will help user to find out friends locations as well as the distance from user’s location. The proposed system will also allow user to see all friends on Google map as well.

The motivation for every location based information system is: “To assist with the exact information, at right place in real time with personalized setup and location sensitiveness”. In this era we are dealing with palmtops and iPhones, which are going to replace the bulky desktops even for computational purposes. We have vast number of applications and usage where a person sitting in a roadside café needs to get relevant data and information. Such needs can only be catered with the help of LBS. These applications include security related jobs, general survey regarding traffic patterns, decision based on vehicular information for validity of registration and license numbers etc. A very appealing application includes surveillance where instant information is needed to decide if the people being monitored are any real threat or an erroneous target. We have been able to create a number of different applications where we provide the user with information regarding a place he or she wants to visit. But these applications are limited to desktops only. We need to import them on mobile devices. Toggles WiFi on/off with proximity to networks, using cell towers recorded while this service is running and connected to those networks.

This application makes use of the Bluetooth & GPS features of mobile phone as a solution for Vehicle tracking system. It is the technology used to determine the location of a vehicle using different methods like GPS and other radio navigation systems operating through satellites and ground based stations. Even data can be stored and downloaded to a computer from the GPS unit at a base station and that can later be used for analysis. This system is an important tool for tracking each vehicle at a given period of time and now it is becoming increasingly popular for people having expensive cars and hence as a theft prevention and retrieval device. No doubt, Vehicle tracking system whether it is GPS based or any other wireless medium has brought one of the most important technological advances in today’s communication field. Now one doesn’t have to leave a place to know where a particular vehicle is at a given period of time. The automatic vehicle locating system only with the help of a tinny electronic device and tracking software can detect the real-time location of a vehicle by using the conventional cell phone network and Internet.

Traffic monitoring systems deployed until now, use data collected mainly through fixed sensors. Advances on the modern mobile devices have made possible the development of Smart Traffic Systems, which use the traffic information gathered by the drivers’ mobile devices to provide route guidance. Our work is focused on building a Real-Time Traffic Information System based mobile devices, which are used for both acquiring traffic information data and for providing feedback and guidance to drivers. This project presents an analysis of the system, its security risks and requirements for dynamic route guidance together with possible solutions. A key component of the system is the mobile application that gathers data in an encrypted way and displays information to the users. The Project is developed using Android and works on any android enabled device.

This application makes use of the Bluetooth & GPS features of mobile phone as a solution for Vehicle tracking system. It is the technology used to determine the location of a vehicle using different methods like GPS and other radio navigation systems operating through satellites and ground based stations. Even data can be stored and downloaded to a computer from the GPS unit
at a base station and that can later be used for analysis. This system is an important tool for tracking each vehicle at a given period of time and now it is becoming increasingly popular for people having expensive cars and hence as a theft prevention and retrieval device. No doubt, Vehicle tracking system whether it is GPS based or any other wireless medium has brought one of the most important technological advances in today’s communication field. Now one doesn’t have to leave a place to know where a particular vehicle is at a given period of time. The automatic vehicle locating system only with the help of a tinny electronic device and tracking software can detect the real-time location of a vehicle by using the conventional cell phone network and Internet.

An Android phone is a handheld computer, a music player, a notepad, a GPS navigation unit and more, all rolled into one sleek device that fits in your pocket. Today’s phones do so many things for us that sometimes we don’t even think about how we do them. Even though our phones do all these new things, the most natural way of interacting with a phone remains what it always has been: speaking. And to that end, we’re pleased to introduce Voice Actions for Android . Voice Actions are a series of spoken commands that let you control your phone using your voice. Call businesses and contacts, send texts and email, listen to music, browse the web, and complete common tasks, all just by speaking into your phone.

Based on the latest technology, we offer GPS for asset outdoor tracking systems also. These devices are based on advanced research & ensures good performance. A asset tracking system is an electronic device installed in a asset to enable the owner or a third party to track the asset's location. Most modern asset tracking systems use Global Positioning System (GPS) modules for accurate location of the asset. Many asset tracking systems also combine a communications component such as cellular or satellite transmitters to communicate the asset’s location to a remote user. Asset information can be viewed on electronic maps via the Internet or specialized software.

This application makes use of the Bluetooth & GPS features of mobile phone as a solution for vehicle automation. Controller–area network (CAN or CAN-bus) is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other within a vehicle without a host computer. In our project we are going to implement CAN based vehicle automation by using Bluetooth,GPS,GSM and Finger print for user security.

This application makes use of the Bluetooth & GPS features of mobile phone as a solution for Vehicle theft control system. This paper deals with the design & development of a theft control system for an automobile, which is being used to prevent/control the theft of a vehicle. The developed system makes use of an embedded system based on GSM technology. The designed & developed system is installed in the vehicle. An interfacing mobile is also connected to the microcontroller, which is in turn, connected to the engine. Once, the vehicle is being stolen, the information is being used by the vehicle owner for further processing. The information is passed onto the central processing insurance system, where by sitting at a remote place, a particular number is dialed by them to the interfacing mobile that is with the hardware kit which is installed in the vehicle. By reading the signals received by the mobile, one can control the ignition of the engine; say to lock it or to stop the engine immediately. Again it will come to the normal condition only after entering a secured password. The owner of the vehicle & the central processing system will know this secured password. The main concept in this design is introducing the mobile communications into the embedded system. The designed unit is very simple & low cost.

This paper presents the technical and technological concept behind a personal information guide (PIG), with which conventional Internet services for mobile applications can be made available on the basis of location based services (LBS). The concept of the PIG mobile client is first explained taking a brief situation analysis of the state of mobile applications and the resulting requirements as the point of departure. Then the focus is on presenting the PIG server infrastructure, which is based on agent, XML and peer-to-peer technology.

When you travel, there are a lot of emotions and memory which you would get. Everyone wants to retain forever in their minds. With the technological advancements, there are a lot of tools that could remain the moment. Through the different platforms of the ways to connect and Location Base Service, the moment it could be stored by digital image, video, text or others. Combination of GPS positioning system tracks out at any time while you want to remain the moment. The research brings up an effective method to store the valuable moment. The method uses expression ways, such as Blog and Google Earth to show the time, locations and events during the trip. Its is a real-time recording system that you can share diary, videos and pictures with your friends. In the system, we design a path algorithm which uses the weighted method to minimize the cost of distance of travel planning. It can be a reference basis for travel planning.

Employee monitoring system using android mobile is, essentially, software that allows Managers to monitor their Employee's office cell phone. All incoming and outgoing calls, texts and multimedia messages can be seen and interrupted by the Managers, who can also monitor where their Employee are (through GPS), access a history of where they've been and set up alerts if their Employee are going outside of approved geographical zones, are receiving texts from unapproved numbers or calls from banned persons. This system uses Android based mobile phones for the software to be run. The mobile device in the hand of the Employee should be an Android based device and the Managers may have any kind mobile devices, since they are going to receive alerts from the Employee in SMS format only. Managers may later login into the centralized server and view the details of their Employee’s mobile usage. This system is really very helpful for the Managers to monitor their Employee through mobile phones.

The disabled have settled down as smart phone users in this age as users of these phones have exponentially increased in recent years. The theme of this paper is how to create a better world using the information that people want to exchange with each other between the disabled and the general population. On the other hand, the main goal is to provide the information that they need from each other which can be displayed on the map in real-time. We propose a new location-based SNS application for the physically disabled population having three major characteristics of this application to be considered as follows: One uses Social Networking Service (SNS) by constructing a friend matching system such as Face book and Twitter, which are the most widely-used SNS in the world, the general population registers real-time information of a specific location on the map for the physically disabled population using SNS. This information with photos and messages is given and evaluated by users, and this system makes it easier to see that the menu in the GUI was implemented.

We developed an Android application that assists drivers/walkers to create texts for internet services under eyes-free and hands-free conditions. The constructed app can create typical texts generated from templates only through spoken dialogues with users at the users desired timing. We also implemented the mode in which one can enter a free formatted text via voice. This spoken dialogue application is realized as a multi-domain spoken dialogue system. Users of this app can utilize two kinds of ways to enter texts in natural dialogues. The structure of this app is extensible for future demands such as adding text templates other than what is currently implemented.

Android platform is a new generation of smart mobile phone platform launched by Google. Android provides the support of mobile map and location service, which is probably a concern of vast numbers of developers. So far, the development of mobile map and location applications is complex and difficult, and is often required to pay high copyright fees to map makers. Android is free and open, providing an easy-to-use development kit containing flexible map display and control functions. This paper introduces the architecture and component models of Android, and analyzes the anatomy of an Android application including the functions of Activity, Intent Receiver, Service, Content Provider, and etc. Based on Android, the design method of a location-based mobile service is then presented. The design example shows that it's so easy to implement self-location, to draw the driving trace, to perform query and to flexibly control the real-time map on Android.

On the impact of the node mobility, location service approaches based on the node location management incur high protocol overhead and low validity of location information. Therefore, a mobile location agent (MLA) algorithm is introduced to optimize location service. For each node, a location agent will be used to perform the location update and query by location service to decrease the protocol overhead. The location agent will assist the packet routing to the destination, which will upgrade the validity of location information caching in the network and improve the performance of geographic routing. When analyzing routing according to the location agent, we show a new method traversing all local closest nodes around a void location agent region to obtain the location information of the destination node. We select representative location service protocols and perform MLA algorithm. Simulation experimental results show that MLA algorithm can significantly reduce the protocol overhead of location service and improve the packet delivery ratio of geographic routing effectively.

In this article, a new mobile Cloud service model is presented. It offers a dynamic and efficient remote access to information services and resources for mobile devices. Mobile Cloud computing has been evolved as a distributed service model, where individual mobile users are Cloud service providers. Compared to traditional Internet-centric Cloud service models, the complexity of mobile service management in a dynamic and distributed service environment is increased dramatically. To address this challenge, we propose to establish an OSGi-based mobile Cloud service model — MCC-OSGi — that uses OSGi Bundles as the basic mobile Cloud service building components. The proposed solution supports OSGi bundles running on both mobile devices and Cloud-side virtual machine OS platforms, and the bundles can be transferred and run on different platforms without compatibility issues. The presented solution is achieved: 1) by incorporating OSGi into Android software development platform, 2) by setting up a Remote-OSGi on the Cloud and on mobile devices, and 3) by defining three service architecture models. The presented solution is validated through a demonstrative application with relevant performance measurements.

Most of today's state-of-the-art processors for mobile and embedded systems feature on-chip scratchpad memories. To efficiently exploit the advantages of low-latency high-bandwidth memory modules in the hierarchy, there is the need for programming models and/or language features that expose such architectural details. On the other hand, effectively exploiting the limited on-chip memory space requires the programmer to devise an efficient partitioning and distributed placement of shared data at the application level. In this paper, we propose a programming framework that combines the ease of use of OpenMP with simple, yet powerful, language extensions to trigger array data partitioning. Our compiler exploits profiled information on array access count to automatically generate data allocation schemes optimized for locality of references.

Smart home is regarded as an independent healthy living for elderly person. Advances in phone technology and new style of computing paradigm (i.e., cloud computing) permits real time acquisition, processing, and tracking of activities in smart home. In this paper, we develop android smartphone application to assists elderly people for independent living in their own homes. It reduces the health expenditures and burden of health care professionals in care facility units. We assume smart home as an intelligent agent to perceive the environment and process the sensory data on cloud. Smartphone application communicates with cloud through web services and assists the elderly person to complete their daily life activities. It facilitates the care giver assistant by tracking the elderly persons in their own homes and avoids certain accidents. Furthermore, it also helps the family members to track the activities, when they are outside from homes.

We examine a technology called C2DM (Cloud to Device Messaging) and how well it integrates with cloud computing. In our investigation we look at the performance of the library, integration with Google App Engine and also the development tools including the API. We create an application using C2DM and do initial performance tests. In an attempt at making development of applications for push messaging on the Android platform simpler, and C2DM in particular, we introduce a new open source library we call Simple-C2DM.

Most of today's state-of-the-art processors for mobile and embedded systems feature on-chip scratchpad memories. To efficiently exploit the advantages of low latency high-bandwidth memory modules in the hierarchy, there is the need for programming models and/or language features that expose such architectural details. On the other hand, effectively exploiting the limited on-chip memory space requires the programmer to devise an efficient partitioning and distributed placement of shared data at the application level. In this paper, we propose a programming framework that combines the ease of use of OpenMP with simple, yet powerful, language extensions to trigger array data partitioning. Our compiler exploits profiled information on array access count to automatically generate data allocation schemes optimized for locality of references

Video surveillance systems are becoming increasingly important for crime investigation and the number of cameras installed in public space is increasing. However, many cameras installed at fixed positions are required to observe a wide and complex area. In order to efficiently observe such a wide area at lower cost, mobile robots are an attractive option.
This paper presents architecture to improve surveillance applications based on the usage of the service oriented paradigm, with android smart phones as user terminals, allowing application dynamic composition and increasing the flexibility of the system.
According to the result of moving object detection research on video sequences, the movement of the people is tracked using video surveillance. The moving object is identified using the image subtraction method. The background image is subtracted from the foreground image. From that the moving object is derived.

It is now feasible to host basic web services on a smart phone due to the advances in wireless devices and mobile communication technologies. While the applications are quite welcoming, the ability to provide secure and reliable communication in the vulnerable and volatile mobile ad-hoc topologies is vastly becoming necessary. The paper mainly addresses the details and issues in providing secured communication and access control for the mobile web service provisioning domain. While the basic message-level security can be provided, providing proper access control mechanisms for the Mobile Host still poses a great challenge. This paper discusses details of secure communication and proposes the distributed semantics-based authorization mechanism.

One in four people in a healthcare facility has a pressure ulcer (bedsore) at any given time, and bedsores are one of the leading iatrogenic causes of death reported in developed countries. Standardized documentation is identified as a critical component in the prevention and treatment of pressure ulcers, with the greatest challenges being non-compliance to protocol and inconsistency of documentation. As a result, attention is focused on electronic information systems, and the research objective in this work was to develop an interactive software application on a mobile device (Smartphone; tablet) to allow healthcare workers to electronically document patients' wounds, and to explore whether the application may promote higher consistency and compliance in wound care documentation, and higher patient and caregiver satisfaction relative to paper-based documentation. A prototype application on an Android platform is in progress with additional intelligence over paper-based forms. The prototype is being extended to a version designed as an educational tool for undergraduate nursing students learning clinical practices in wound care. The work advances the emerging area of healthcare applications and supports the increasing prevalence of e-health in nursing practice.

High relapse rate is one of the most prominent problems in addiction treatment. Alcohol Resilience Treatment (ART), an alcohol addiction therapy, is based on Cue Exposure Treatment, which has shown promising results in preliminary studies. ART aims at optimizing the core area of relapse prevention, and intends to improve patients' capability to withstand craving of alcohol. This method emphasizes the interplay of resilience and resourcefulness. It contains 6 sessions with different topics according to the stage of treatment circuit, and each session consists of 6 steps. Due to the purity and structure of the treatment rationale, it is realistic, reasonable and manageable to transform the method into a smartphone application. An ART app in Android system and an accessory of bilateral tactile stimulation were developed and will be used in a study with behavioral self-control training. This paper presents the design and realization of the smartphone based ART application. The design of a pilot study, which is to examine the benefits of a smartphone application providing behavioral self-control training, is also reported in this paper.

The rapidly aging population not only causes long hospital waiting times and expensive hospital stays, but also increases the workload of doctors and medical practitioners. Managing the cost and quality of treatment and caring for seniors are becoming key pressing issues in both Developed and developing countries. Diagnosing and continuous record of real-time data by the use of portable patient monitoring system during normal activity would be beneficial for medical practitioners to do proper and better treatment also it would be useful for health care providers to improve diseases management . Bluetooth technology is used for data retrieval from Database. Bluetooth has been specifically designed as a low-cost, low-power, and small-size radio technology, which is particularly dedicated to short-range communications.

Screen capturing tool for Android handsets connected via USB to a desktop/laptop. It is great for full screen presentations, product demos, automatic screen recording, or just a single screenshot. Without root.

This research paper illustrates an adaptive outsourcing algorithm that runs on mobile phones. The algorithm outsources the computational load that needs to be done on a sensor data (in this case an image) to a server, based on many criteria, where the execution takes place. The end result is sent back to the mobile phone and presented to the user. The results from these measurements have shown up to 85 % decrease in power consumption and up to 60% decrease in latency compared to locally executing on the mobile phone in many situations. Moreover, outsourcing using a WiFi connection has proved to be favourable in most scenarios where complex algorithms are applied on the image, while a 3G connection has showed a lot of discrepancy from one situation to another.