Nowadays we are faced with an increasing popularity of social software including wikis, blogs, micro-blogs and online social networks such as Facebook and MySpace. Unfortunately, the mostly used social services are centralized and personal information is stored at a single vendor. This results in potential privacy problems as users do not have much control over how their private data is disseminated. To overcome this limitation, some recent approaches envisioned replacing the single authority centralization of services by a peer-to-peer trust-based approach where users can decide with whom they want to share their private data. In this peer-to-peer collaboration it is very difficult to ensure that after data is shared with other peers, these peers will not misbehave and violate data privacy. In this paper we propose a mechanism that addresses the issue of data privacy violation due to data disclosure to malicious peers. In our approach trust values between users are adjusted according to their previous activities on the shared data. Users share their private data by specifying some obligations the receivers must follow. We log modifications done by users on the shared data as well as the obligations that must be followed when data is shared. By a log-auditing mechanism we detect users that misbehaved and we adjust their associated trust values by using any existing decentralized trust model.
Trust management is an important aspect of the solution that we proposed. The concept of trust in different communities varies according to how it is computed and used. Our work relies on the concept of trust which is based on past encounters: Trust is a subjective expectation an agent has about another's future behavior based on the history of their encounters'' MuiHICSS02 . Various trust models for peer to peer systems exist such as NICE model @cite_12 , EigenTrust model @cite_5 and global trust model @cite_16 and our mechanism for discovering misbehaved users can be coupled with any existing trust model in order to manage user trust values.
@cite_5 @cite_16 @cite_12
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Managing trust is a problem of particular importance in peer-to-peer environments where one frequently encounters unknown agents. Existing methods for trust management, that are based on reputation, focus on the semantic properties of the trust model. They do not scale as they either rely on a central database or require to maintain global knowledge at each agent to provide data on earlier interactions. In this paper we present an approach that addresses the problem of reputation-based trust management at both the data management and the semantic level. We employ at both levels scalable data structures and algorithms that require no central control and allow to assess trust by computing an agents reputation from its former interactions with other agents. Thus the meethod can be implemented in a peer-to-peer environment and scales well for very large numbers of participants. We expect that scalable methods for trust management are an important factor, if fully decentralized peer-to-peer systems should become the platform for more serious applications than simple file exchange. The concept of trust and or trust management has received considerable attention in engineering research communities as trust is perceived as the basis for decision making in many contexts and the motivation for maintaining long-term relationships based on cooperation and collaboration. Even if substantial research effort has been dedicated to addressing trust-based mechanisms or trust metrics (or computation) in diverse contexts, prior work has not clearly solved the issue of how to model and quantify trust with sufficient detail and context-based adequateness. The issue of trust quantification has become more complicated as we have the need to derive trust from complex, composite networks that may involve four distinct layers of communication protocols, information exchange, social interactions, and cognitive motivations. In addition, the diverse application domains require different aspects of trust for decision making such as emotional, logical, and relational trust. This survey aims to outline the foundations of trust models for applications in these contexts in terms of the concept of trust, trust assessment, trust constructs, trust scales, trust properties, trust formulation, and applications of trust. We discuss how different components of trust can be mapped to different layers of a complex, composite network; applicability of trust metrics and models; research challenges; and future work directions. We identify the trust management problem as a distinct and important component of security in network services. Aspects of the trust management problem include formulating security policies and security credentials, determining whether particular sets of credentials satisfy the relevant policies, and deferring trust to third parties. Existing systems that support security in networked applications, including X.509 and PGP, address only narrow subsets of the overall trust management problem and often do so in a manner that is appropriate to only one application. This paper presents a comprehensive approach to trust management, based on a simple language for specifying trusted actions and trust relationships. It also describes a prototype implementation of a new trust management system, called PolicyMaker, that will facilitate the development of security features in a wide range of network services. Trust between a pair of users is an important piece of information for users in an online community (such as electronic commerce websites and product review websites) where users may rely on trust information to make decisions. In this paper, we address the problem of predicting whether a user trusts another user. Most prior work infers unknown trust ratings from known trust ratings. The effectiveness of this approach depends on the connectivity of the known web of trust and can be quite poor when the connectivity is very sparse which is often the case in an online community. In this paper, we therefore propose a classification approach to address the trust prediction problem. We develop a taxonomy to obtain an extensive set of relevant features derived from user attributes and user interactions in an online community. As a test case, we apply the approach to data collected from Epinions, a large product review community that supports various types of interactions as well as a web of trust that can be used for training and evaluation. Empirical results show that the trust among users can be effectively predicted using pre-trained classifiers.