Survey on Mobile Social Cloud Computing (MSCC)

Due to enhancement in technology the use of mobile devices increases with time. Now mobile devices (mobiles, PDA, Laptops etc.) became an essential part of mankind’s life. With the ease of Internet the popularity of Social Networking Services (SNS) among people increases. With the sharp drops in the prices, the working of mobile devices including smart phones and laptops is rising steadily. So due to this, mobile devices are now used as a provider of computing resources and services instead of requester. For this concept of Cloud Computing (CC) is merged with the mobile computing and SNS which is known as MSCC. MSCC is technology of future and it enables users/consumers to access the services in a fast and efficient manner. MSCC is the integration of three different technologies 1) Mobile Computing 2) SNS 3) Cloud Computing. Here mobile devices are (those have moments) using SNS (Both as a provider or requester) in Cloud Computing (CC) environment. In such environment, a user through mobile devices canparticipate in a social network through relationships which are based on trust. Units of the identical or alike social network can share services or data of cloud with other users of that social network without any authentication by using their mobile device as they be members of the identical social network. Various techniques are revised and improved to achieve good performance in a cloud computing network environment. In this work, there is a detailed survey of existing social cloud and mobile cloud techniques and their application areas. The comparative survey tables can be used as a guideline to select a technique suitable for different applications at hand. This survey paper reports the results of a survey of Mobile Social Cloud Computing (MSCC) regarding the importance of security of MSCC. Here we compare the works of different researcher in the field of MSCC on the basis of some essential features like security algorithm used, Qos and Fault tolerant strategy used, ease of proposed algorithm, space complexity etc. Considering all the limitations of the existing social cloud and mobile cloud techniques, an adaptive MSCC framework of Fault tolerance for future research is proposed

A New Authentication Model Based on CL-PKC in Resource limited P2P Systems

This paper proposes a new authentication model based on CL-PKC technology (Certificate less public key cryptography) in peer-to-peer systems. With the progress in peer-to-peer technology, lots of things related to the security problems of peer-to-peer systems have been exposing. To solve these security problems, authentication must be settled firstly. So this paper develops an authentication method based on CL-PKC technology, considering the dynamic properties of hybrid peer-to-peer systems. This method simplifies the procedure of getting public keys and authentication procedure, so the efficiency is increased, and the mount of bandwidth required is lower. This method is very fit to the systems with limited resources

WEB ADMINISTRATION SUGGESTION BY MEANS OF MISUSING AREA AND QOS DATA

Web administrations are incorporated programming segments for the backing of interoperable machine-to-machine association over a system. Web administrations have been broadly utilized for building administration situated applications in both industry and the educated community in late years. The quantity of freely accessible Web administrations is consistently expanding on the Internet. Be that as it may, this multiplication makes it hard for a client to choose an appropriate Web administration among a lot of administration competitors. An improper administration determination may bring about numerous issues (e.g., illsuited execution) to the subsequent applications. In this paper, we propose a novel community separating based Web administration recommender framework to help clients select administrations with ideal Quality-of-Service (QoS) execution. Our recommender framework utilizes the area data and QoS qualities to bunch clients and administrations, and makes customized administration proposal for clients in view of the grouping results. Contrasted and existing administration suggestion techniques, our methodology accomplishes impressive change on the proposal precision. Extensive tests are led including more than 1.5 million QoS records of true Web administrations to exhibit the adequacy of our methodology

SocialLink: a Social Network Based Trust System for P2P File Sharing Systems

In peer-to-peer (P2P) file sharing systems, many autonomous peers without preexisting trust relationships share files with each other. Due to their open environment and distributed structure, these systems are vulnerable to the significant impact from selfish and misbehaving nodes. Free-riding, whitewash, collusion and Sybil attacks are common and serious threats, which severely harm non-malicious users and degrade the system performance. Many trust systems were proposed for P2P file sharing systems to encourage cooperative behaviors and punish non-cooperative behaviors. However, querying reputation values usually generates latency and overhead for every user. To address this problem, a social network based trust system (i.e., SocialTrust) was proposed that enables nodes to first request files from friends without reputation value querying since social friends are trustable, and then use trust systems upon friend querying failure when a node\u27s friends do not have its queried file. However, trust systems and SocialTrust cannot effectively deal with free-riding, whitewash, collusion and Sybil attacks. To handle these problems, in this thesis, we introduce a novel trust system, called SocialLink, for P2P file sharing systems. By enabling nodes to maintain personal social network with trustworthy friends, SocialLink encourages nodes to directly share files between friends without querying reputations and hence reduces reputation querying cost. To guarantee the quality of service (QoS) of file provisions from non-friends, SocialLink establishes directionally weighted links from the server to the client with successful file transaction history to constitute a weighted transaction network , in which the link weight is the size of the transferred file. In this way, SocialLink prevents potential fraudulent transactions (i.e., low-QoS file provision) and encourages nodes to contribute files to non-friends. By constraining the connections between malicious nodes and non-malicious nodes in the weighted transaction network, SocialLink mitigates the adverse effect from whitewash, collusion and Sybil attacks. By simulating experiments, we demonstrate that SocialLink efficiently saves querying cost, reduces free-riding, and prevents damage from whitewash, collusion and Sybil attacks

Computing multi-dimensional trust by mining E-commerce feedback comments

Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The “all good reputation” problem however is prevalent in current reputation systems - reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this thesis, based on the observation that buyers often express opinions openly in free text feedback comments, we have proposed CommTrust, a multi-dimensional trust evaluation model, for computing comprehensive trust profiles for sellers in e-commerce applications. Different from existing multi-dimensional trust models, we compute dimension trust scores and dimension weights automatically via extracting dimension ratings from feedback comments. Based on the dependency relation parsing technique, we have proposed Lexical-LDA (Lexical Topic Modelling based approach) and DR-mining (Lexical Knowledge based approach) approaches to mine feedback comments for dimension rating profiles. Both approaches achieve significantly higher accuracy for extracting dimension ratings from feedback comments than a commonly used opinion mining approach. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively. To the best of our knowledge, our research demonstrates the novel application of combining natural language processing with opinion mining and summarisation techniques in trust evaluation for e-commerce applications

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources