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1. Data integrity and security using cloud computing
Project Code : JCC1601                    Year : 2016 (IEEE)

Abstract : 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).

2.  Extra security using graphical password to cloud
Project Code : JCC1602                       Year : 2016 (IEEE)

ABSTRACT : Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. In this paper, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.

3. A Distributed Video Management Cloud Platform
Project Code : JCC1603                          Year : 2016 (IEEE)

ABSTRACT The rapidly increasing power of personal mobile devices 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.

4. Cloud computing single cloud to multi cloud system.
Project Code : JCC1604                     Year : 2016 (IEEE)

Abstract :  With the rise of various cloud services, the problem of redundant data is more prominent in the cloud storage systems. How to assign a set of documents to a distributed file system, which can not only reduce storage space, but also ensure the access efficiency as much as possible, is an urgent problem which needs to be solved. Space-efficiency mainly uses data de-duplication technologies, while access-efficiency requires gathering the files with high similarity on a server. Based on the study of other data de-duplication technologies, especially the Similarity-Aware Partitioning (SAP) algorithm, this paper proposes the Frequency and Similarity-Aware Partitioning (FSAP) algorithm for cloud storage. The FSAP algorithm is a more reasonable data partitioning algorithm than the SAP algorithm. Meanwhile, this paper proposes the Space-Time Utility Maximization Model (STUMM), which is

useful in balancing the relationship between space-efficiency and access-efficiency. Finally, this paper uses 100 web files downloaded from CNN for testing, and the results show that, relative to using the algorithms associated with the SAP algorithm (including the SAP-Space-Delta algorithm and the SAP-Space-Dedup algorithm), the

FSAP algorithm based on STUMM reaches higher compression ratio and a more balanced distribution of data blocks.

5. Visual cryptography for biometric privacy in cloud.
Project Code : JCC1605                       Year : 2016 (IEEE)

Abstract — In this era due to unbelievable development in internet, various online attacks has been increased. From all such attacks most popular attack is phishing. This attacks are done for extracting confidential information such as banking information, passwords from unsuspecting victims for fraud purposes. Confidential data can’t be directly uploaded on website since it is risky. Here in this paper data is encrypted in video and visual cryptography for login purpose in our online database system for providing more security .

6. Eye Retina Authentication Cloud Computing. 
Project Code : JCC1606                            Year : 2016 (IEEE)

Abstract— Cloud computing has been envisioned as the next-generation architecture of IT enterprise. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, cloud computing moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. In this article, we focus on cloud secure data storage, which has always been an important aspect of quality of service (QOS). To ensure the secure storage of u ser’s data in the cloud, we propose an effective and flexible bio metric authentication using face.

7. Privacy Preserving and Delegated Access Control for Cloud Applications.
Project Code : JCC1607                      Year : 2016 (IEEE)

ABSTRACT : 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 Attributebased 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.

8. Privacy aware data aggregation in mobile sensing.
Project Code : JCC1608                       Year : 2016 (IEEE)

ABSTRACT: The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. Although there are some existing works in this area, they either require bidirectional communications between the aggregator and mobile users in every aggregation period, or have high-computation overhead and cannot support large plaintext spaces. Also, they do not consider the Min aggregate, which is quite useful in mobile sensing. To address these problems, we propose an efficient protocol to obtain the Sum aggregate, which employs an additive homomorphic encryption and a novel key management technique to support large plaintext space. We also extend the sum aggregation protocol to obtain the Min aggregate of time-series data. To deal with dynamic joins and leaves of mobile users, we propose a scheme that utilizes the redundancy in security to reduce the communication cost for each join and leave. Evaluations show that our protocols are orders of magnitude faster than existing solutions, and it has much lower communication overhead.


9. Web Service QoS Prediction Based on Adaptive Dynamic Programming Using Fuzzy Neural Networks for Cloud Services.
Project Code : JCC1609                       Year : 2016 (IEEE)

ABSTRACT :  Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of cloud computing infrastructures is required to predict and quantify the cost-benefit of a strategy portfolio and the corresponding quality of service (QoS) experienced byusers. Such analyses are not feasible by simulation or on-the-field experimentation, due to the great number of parameters that have to be investigated. In this paper, we present an analytical model, based on stochastic reward nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach is presented that,starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions

10. Online java complier in cloud computing.
Project Code : JCC1610                       Year : 2016 (IEEE)

Abstract : As it is a competitive world and very fast world, every thing in the universes is to be internet. In this internet world all the things are on-line. So we created software called “On-line java compiler with security editor”.
The main aim of this project we can easily to write a java program and compile it and debug in on-line. The client machine doesn’t having java development kit .The client machine only connected to the server.  The server having java compiler .so server executes the java code and produce the error message to the appropriate client machine.
In this project is also creating a security editor. This editor performs Encrypt and decrypts the file. Encryption and decryption process perform using RSA Algorithms. There is lot of security algorithms are there, but RSA algorithm is very efficient to encrypt and decrypt the file.
In this project is used to view all type of java API .It is very useful for writing the java program easily, for example if any error in the format of API means we can able to view API throw this modules

11. Frequency and Similarity-Aware Partitioning for Cloud Storage Based on space time utility maximization model.
Project Code : JCC1611                       Year : 2016 (IEEE)

Abstract::  With the rise of various cloud services, the problem of redundant data is more prominent in the cloud storage systems. How to assign a set of documents to a distributed file system, which can not only reduce storage space, but also ensure the access efficiency as much as possible, is an urgent problem which needs to be solved. Space-efficiency mainly uses data de-duplication technologies, while access-efficiency requires gathering the files with high similarity on a server. Based on the study of other data de-duplication technologies, especially the Similarity-Aware Partitioning (SAP) algorithm, this paper proposes the Frequency and Similarity-Aware Partitioning (FSAP) algorithm for cloud storage. The FSAP algorithm is a more reasonable data partitioning algorithm than the SAP algorithm. Meanwhile, this paper proposes the Space-Time Utility Maximization Model (STUMM), which is

useful in balancing the relationship between space-efficiency and access-efficiency. Finally, this paper uses 100 web files downloaded from CNN for testing, and the results show that, relative to using the algorithms associated with the SAP algorithm (including the SAP-Space-Delta algorithm and the SAP-Space-Dedup algorithm), the

FSAP algorithm based on STUMM reaches higher compression ratio and a more balanced distribution of data blocks

12. An Inline High Performance Deduplication System Used in Cloud Storage.
Project Code : JCC1612                       Year : 2016 (IEEE)

Abstract: :  Data deduplication is an emerging and widely employed method for current storage systems. As this technology is gradually applied in inline scenarios such as with virtual machines and cloud storage systems, this study proposes a novel deduplication architecture called I-sieve. The goal of I-sieve is to realize a high performance data sieve system based on iSCSI in the cloud storage system. We also design the corresponding index and mapping tables and present a multi-level cache using a solid state drive to reduce RAM consumption and to optimize lookup performance. A prototype of I-sieve is implemented based on the open source iSCSI target, and many experiments have been conducted driven by virtual machine images and testing tools. The evaluation results show excellent deduplication and foreground performance. More importantly, I-sieve can co-exist with the existing

deduplication systems as long as they support the iSCSI protocol.

13. Data Security in Cloud Architecture Based Elliptical Curve Cryptography.
Project Code : JCC1613                       Year : 2016 (IEEE)

Abstract — Secure and efficient data storage is needed in the cloud environment in modern era of information technology industry. In the present scenario the cloud verifies the authenticity of the cloud services without the knowledge of user’s identity. The cloud provides massive data access directly through the internet. Centralized storage mechanism is followed here for effective accessing of data. Cloud service providers are normally acquires the software and hardware resources and the cloud consumers are avail the services through the internet access in lease basis. Cloud security is enhanced through cryptography technique applied to the cloud security to avoid vulnerability. The intractable computability is achieved in the cloud by using the public key cryptosystem. This paper proposed the approach of applying Hyper elliptic curve cryptography for data protection in the cloud with the small key size. The proposed system has the further advantage of eliminating intruder in cloud computing. Efficacy of the system is to provide the high security of the cloud data.

14. A Cloud Service Architecture for Analyzing Big Monitoring Data.
Project Code : JCC1614                       Year : 2016 (IEEE)

Abstract : Cloud monitoring is of a source of big data that are constantly produced from traces of infrastructures, platforms, and applications. Analysis of monitoring data delivers insights of the system’s workload and usage pattern and ensures workloads are operating at optimum levels. The analysis process involves data query and

extraction, data analysis, and result visualization. Since the volume of monitoring data is big, these operations require a scalable and reliable architecture to extract, aggregate, and analyze data in an arbitrary range of granularity. Ultimately, the results of analysis become the knowledge of the system and should be shared and

communicated. This paper presents our cloud service architecture that explores a search cluster for data indexing and query. We develop REST APIs that the data can be accessed by different analysis modules. This architecture enables extensions to integrate with software frameworks of both batch processing (such as Hadoop) and stream processing (such as Spark) of big data. The analysis results are structured in Semantic Media Wiki pages in the context of the monitoring data source and the analysis process. This cloud architecture is empirically assessed to evaluate its responsiveness when processing a large set of data records under node failures.

15. Fog computing : A new concept By cisco.
Project Code : JCC1615                       Year : 2016 (IEEE)

ABSTRACT :  Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. We propose a different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user’s real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment.

16. Cloud Data Protection for the Masses.
Project Code : JCC1616                       Year : 2016 (IEEE)

Abstract : Offering strong data protection to cloud users while enabling rich applications is a challenging task. We explore a new cloud platform architecture called Data Protection as a Service, which dramatically reduces the per-application development effort required to offer data protection, while still allowing rapid development and maintenance.

17. Ensuring Distributed Accountability For Data Sharing In The Cloud.
Project Code : JCC1617                       Year : 2016 (IEEE)

Abstract :  Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. A major feature of the cloud services is that users’ data are usually processed remotely in unknown machines that users do not own or operate. While enjoying the convenience brought by this new emerging technology, users’ fears of losing control of their own data (particularly, financial and health data) can become a significant barrier to the wide adoption of cloud services. To address this problem, here, we propose a novel highly decentralized information accountability framework to keep track of the actual usage of the users’ data in the cloud. In particular, we propose an object-centered approach that enables enclosing our logging mechanism together with users’ data and policies. We leverage the JAR programmable capabilities to both create a dynamic and traveling object, and to ensure that any access to users’ data will trigger authentication and automated logging local to the JARs. To strengthen user’s control, we also provide distributed auditing mechanisms. We provide extensive experimental studies that demonstrate the efficiency and effectiveness of the proposed approaches.

18. Cooperative Provable Data Possession for Integrity Verification in Multi-Cloud Storage.
Project Code : JCC1618                        Year : 2016 (IEEE)

ABSTRACT
Provable data possession (PDP) is a technique for ensuring the integrity of data in storage outsourcing. In this paper, we address the construction of an efficient PDP scheme for distributed cloud storage to support the scalability of service and data migration, in which we consider the existence of multiple cloud service providers to cooperatively store and maintain the clients’ data. We present a cooperative PDP (CPDP) scheme based on homomorphic verifiable response and hash index hierarchy. We prove the security of our scheme based on multi-prover zero-knowledge proof system, which can satisfy completeness, knowledge soundness, and zero-knowledge properties. In addition, we articulate performance optimization mechanisms for our scheme, and in particular present an efficient method for selecting optimal parameter values to minimize the computation costs of clients and storage service providers. Our experiments show that our solution introduces lower computation and communication overheads in comparison with non-cooperative approaches.

19. A Secure Erasure Code-Based Cloud Storage System with Secure Data Forwarding.
Project Code : JCC1619                       Year : 2016 (IEEE)

ABSTRACT:  A cloud storage system, consisting of a collection of storage servers, provides long-term storage services over the Internet. Storing data in a third party’s cloud system causes serious concern over data confidentiality. General encryption schemes protect data confidentiality, but also limit the functionality of the storage system because a few operations are supported over encrypted data. Constructing a secure storage system that supports multiple functions is challenging when the storage system is distributed and has no central authority. We propose a threshold proxy re-encryption scheme and integrate it with a decentralized erasure code such that a secure distributed storage system is formulated. The distributed storage system not only supports secure and robust data storage and retrieval, but also lets a user forward his data in the storage servers to another user without retrieving the data back. The main technical contribution is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted messages. Our method fully integrates encrypting, encoding, and forwarding. We analyze and suggest suitable parameters for the number of copies of a message dispatched to storage servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment between the number of storage servers and robustness.


20. Scalable and Secure Sharing of Personal Health Records in Cloud Computing using Attribute-based Encryption.
Project Code : JCC1620                       Year : 2016 (IEEE)

ABSTRACT:  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

21. Portable Organization Cloud Services .
Project Code : JCC1621                       Year : 2016 (IEEE)

ABSTRACT : Underneath all the hype, the essence of cloud computing is the industrialization of IT. Similar to mass production lines in other industries (such as the auto industry), cloud computing standardizes offered services and thus increases automation significantly. Consequently, enterprises are increasingly utilizing cloud technology; however, major challenges such as portability, standardization of service definitions, and security remain inadequately addressed. The ability to move cloud services and their components between providers ensures an adequate and cost-efficient IT environment and avoids vendor lock-in. Research has already addressed movability and migration on a functional level.1,2 However, no one has yet examined cloud service portability with regard to management and operational tasks, which are a significant and increasing cost factor. One reason is the lack of an industry standard for defining composite applications and their management. Without an appropriate standardized format, ensuring compliance, trust, and security  the biggest area of critique preventing the cloud’s wider adoption is difficult. Dealing with these challenges in industry and research has the potential to bring cloud computing to the next level. Here, we present how the portable and standardized management of cloud services is enabled through the Topology and Orchestration Specification for Cloud Applications (TOSCA),3 a recently initiated standardization effort from OASIS. We show how TOSCA plans  which capture the management aspects of cloud services in a reusable way  use existing workflow technologies and research results to facilitate the portable, automated, and reusable management of cloud services throughout their life cycle.


22. Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud.
Project Code : JCC1622                        Year : 2016 (IEEE)

ABSTRACT
Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. We propose a different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user’s real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment.

23. A Cloud Gaming System Based on User-Level Virtualization and Its Resource Scheduling.
Project Code : JCC1623                        Year : 2016 (IEEE)

Abstract —Many believe the future of gaming lies in the cloud, namely Cloud Gaming, which renders an interactive gaming application in the cloud and streams the scenes as a video sequence to the player over Internet. This paper proposes GCloud, a GPU/CPU hybrid cluster for cloud gaming based on the user-level virtualization technology. Specially, we present a performance model to analyze the

server-capacity and games’ resource-consumptions, which categorizes games into two types: CPU-critical and memory-io-critical. Consequently, several scheduling strategies have been proposed to improve the resource-utilization and compared with others. Simulation tests show that both of the First-Fit-like and the Best-Fit-like strategies outperform the other(s); especially they are near optimal in the batch processing mode. Other test results indicate that GCloud is efficient: An off-the-shelf PC can support five high-end video-games run at the same time. In addition, the average per-frame processing delay is 819 ms under different image-resolutions,

which outperforms other similar solutions.

24. A Mobile Offloading Game Against Smart Attacks.
Project Code : JCC1624                       Year : 2016 (IEEE)

ABSTRACT :  Mobile devices, such as smartphones, can ofoad applications and data to the cloud via access points or base stations to reduce energy consumption and improve user experience. However, mobile ofoading is vulnerable to smart attackers that use smart and programmable radio devices, such as universal

software radio peripherals, to perform multiple types of attacks, such as spoong and jamming, based on the radio environment and ofoading transmissions. In this paper, a mobile ofoading game is investigated that consists of three players: a mobile device that chooses its ofoading rate, a smart attacker that determines its attack mode, and a security agent that decides whether or not to initiate full protection for the serving access point during the ofoading. Nash and Stackelberg equilibria of the ofoading game are derived and their existence conditions are discussed. A Q-learning-based mobile ofoading strategy is proposed for mobile devices that are unaware of system parameters, such as the channel conditions, in dynamic radio

environments. Simulation results show that the proposed ofoading strategy can improve the utility of the mobile device and reduce the attack rate of smart attackers.

25. A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data Environment.
Project Code : JCC1625                        Year : 2016 (IEEE)

ABSTRACT:  Effective patient queue management to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals. Unnecessary and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient in the queue, the total treatment time of all the patients before him is the time that he must wait. It would be convenient and preferable if the patients could receive the most efcient treatment plan and know the predicted waiting time through a mobile application that updates in real time. Therefore, we propose a Patient Treatment Time Prediction (PTTP) algorithm to predict the waiting time for each treatment task for a patient. We use realistic patient data from various hospitals to obtain a patient treatment time model for each task. Based on this large-scale, realistic dataset, the treatment time for each patient in the current queue of each task is predicted. Based on the predicted waiting time, a Hospital Queuing-Recommendation (HQR) system is developed. HQR calculates and predicts an efcient and convenient treatment plan recommended for the patient. Because of the large-scale, realistic dataset and the requirement for real-time response, the PTTP algorithm and HQR system mandate efciency and low-latency response. We use an Apache Spark-based cloud implementation at the National Supercomputing Center in Changsha to achieve the aforementioned goals. Extensive experimentation and simulation results demonstrate the effectiveness and applicability of our proposed model to recommend an effective treatment plan for patients to minimize their wait times in hospitals.

26. A Tutorial on Secure Outsourcing of Large-scale Computations for Big Data.
Project Code : JCC1626                       Year : 2016 (IEEE)

ABSTRACT:  Today's society is collecting a massive and exponentially growing amount of data that can potentially revolutionize scientic and engineering elds, and promote business innovations.With the advent of cloud computing, in order to analyze data in a cost-effective and practical way, users can outsource their computing tasks to the cloud, which offers access to vast computing resources on an on-demand and pay-per-use basis. However, since users' data contains sensitive information that needs to be kept secret for ethical, security, or legal reasons, many users are reluctant to adopt cloud computing. To this end, researchers have proposed techniques that enable users to ofoad computations to the cloud while protecting their data privacy. In this paper, we review the recent advances in the secure outsourcing of large-scale computations for a big data analysis. We rst introduce two most fundamental and common computational problems, i.e., linear algebra and optimization, and then provide an extensive review of the data privacy preserving techniques. After that, we explain how researchers have exploited the data privacy preserving techniques to construct secure outsourcing algorithms for large-scale computations.

27. An Anomalous Behavior Detection Model in Cloud Computing.
Project Code : JCC1627                       Year : 2016 (IEEE)

Abstract:  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.

28. An Efficient Privacy-Preserving Ranked Keyword Search Method.
Project Code : JCC1628                       Year : 2016 (IEEE)

Abstract —Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved

documents.

29. Analysis of Classical Encryption Techniques in Cloud Computing.
Project Code : JCC1629                       Year : 2016 (IEEE)

Abstract: Cloud computing has become a significant computing model in the IT industry. In this emerging model, computing resources such as software, hardware, networking, and storage can be accessed anywhere in the world on a pay-per-use basis. However, storing sensitive data on un-trusted servers is a challenging issue for this model. To guarantee confidentiality and proper access control of outsourced sensitive data, classical encryption techniques are used. However, such access control schemes are not feasible in cloud computing because of their lack of flexibility, scalability, and fine-grained access control. Instead, Attribute-Based Encryption (ABE) techniques are used in the cloud. This paper extensively surveys all ABE schemes and creates a comparison table for the key criteria for these schemes in cloud applications.

30. Bayes-Based ARP Attack Detection Algorithm for Cloud Centers.
Project Code : JCC1630                       Year : 2016 (IEEE)

Abstract: To address the issue of internal network security, Software-Defined Network (SDN) technology has been introduced to large-scale cloud centers because it not only improves network performance but also deals with network attacks. To prevent man-in-the-middle and denial of service attacks caused by an address resolution protocol bug in an SDN-based cloud center, this study proposed a Bayes-based algorithm to calculate the probability of a host being an attacker and further presented a detection model based on the algorithm. Experiments were

conducted to validate this method.

31. Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges.
Project Code : JCC1631                Year : 2016 (IEEE)

ABSTRACT :  Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and exibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example,WebSCADA is an application used for

smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security

measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI's, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briey describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard.

32. Efficient Multicast Delivery for Data redundancy minimization over wireless data centers.
Project Code : JCC1632                       Year : 2016 (IEEE)

ABSTRACT:  With the explosive growth of cloud-based services, large-scale data centers are widely built for housing critical computing resources to gain signicant economic benets. In data centers, the cloud services are generally accomplished by multicast-based group communications. Recently, many well-known industries, such as Microsoft, Google, and IBM, adopt high-speed wireless technologies to

augment network capacity in data centers. However, those well-known multicast delivery schemes for traditional wired data centers do not consider the unique characteristics of wireless communications, which may result in unnecessary data transmissions and network congestions. Under the coexisting scenario of wired

and wireless links, this paper studies multicast tree construction and maintenance problems. The objective is to minimize the total multicast trafc. We prove the problems are NP-hard and propose efcient heuristic algorithms for the two problems. Based on real traces and practical settings obtained from commercial data centers, a series of experiments are conducted, and the experimental results showthat our proposed algorithms are effective for reducing multicast data trafc. The results also provide useful insights into the design of multicast tree construction and maintenance for wireless data center networks.

33. Multiagent-Multiobjective Interaction Game System for Service provisoning vehicular cloud.
Project Code : JCC1633                        Year : 2016 (IEEE)

Abstract — The increasing number of applications based on the Internet of Things (IoT), as well as advances in wireless communication, information and communication technology, and mobile cloud computing, has allowed mobile users to access a wider range of resources when mobile. As the use of vehicular cloud computing has become more popular due to its ability to improve driver and vehicle safety, researchers and industry have a growing interest in the design and development of vehicular networks for emerging applications. Vehicle drivers can now access a variety of on demand resources en route via vehicular network service providers. The adaptation of vehicular cloud services faces many challenges, including cost, privacy and latency. The contributions of this paper are as follows: First, we propose a game theory-based framework to manage on-demand service provision in a vehicular cloud. We present three different game approaches, each of which helps drivers minimize their service costs and latency, and maximize their privacy. Secondly, we propose a Quality-of-Experience (QoE) framework for service provision in a vehicular cloud for various types of users; a simple but effective model to determine driver preferences. Third, we propose using the Trusted Third Party (TTP) concept to represent drivers and service providers, and ensure fair game treatment. We develop and evaluate simulations of the proposed approaches under different network scenarios with respect to privacy, service cost and latency, by varying the vehicle density and driver preferences. The results show that the proposed approach outperforms conventional models, since the game theory system introduces a bounded latency of ≤ 3%, achieves service cost savings up to 65%, and preserves driver privacy by reducing revealed information by up to 47%.

34. Toward a Real-Time Framework in Cloudlet-Based Architecture.
Project Code : JCC1634                        Year : 2016 (IEEE)

Abstract: In this study, we present a framework based on a prediction model that facilitates user access to a number of services in a smart living environment. Users must be able to access all available services continuously equipped with mobile devices or smart objects without being impacted by technical constraints such as performance or memory issues, regardless of their physical location and mobility. To achieve this goal, we propose the use of cloudlet-based architecture that serves as distributed cloud resources with specific ranges of influence and a realtime

processing framework that tracks events and preferences of the end consumers, predicts their requirements, and recommends services to optimize resource utilization and service response time.

35. Towards a Virtual Domain Based Authentication on MapReduce.
Project Code : JCC1635                       Year : 2016 (IEEE)

ABSTRACT :  This paper has proposed a novel authentication solution for the MapReduce (MR) model, a new distributed and parallel computing paradigm commonly deployed to process BigData by major IT players, such as Facebook and Yahoo. It identies a set of security, performance, and scalability requirements that are

specied from a comprehensive study of a job execution process using MR and security threats and attacks in this environment. Based on the requirements, it critically analyzes the state-of-the-art authentication solutions, discovering that the authentication services currently proposed for the MR model is not adequate.

This paper then presents a novel layered authentication solution for the MR model and describes the core components of this solution, which includes the virtual domain based authentication framework (VDAF). These novel ideas are signicant, because, rst, the approach embeds the characteristics of MR-in-cloud deployments into security solution designs, and this will allow the MR model be delivered as a software as a service in a public cloud environment along with our proposed authentication solution; second, VDAF supports the authentication of every interactions by any MR components involved in a job execution ow, so long as the interactions are for accessing resources of the job; third, this continuous authentication service is provided in such a manner that the costs incurred in providing the authentication service should be as low as possible.

36. Validation of Pervasive Cloud Task Migration with Colored Petri Net.
Project Code : JCC1636                      Year : 2016 (IEEE)

Abstract: Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net (CPN). In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies: one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.

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