Anonymity networks hide user identities with the help of relayed anonymity routers. However, the state-of-the-art anonymity networks do not provide an effective trust model. As a result, users cannot circumvent malicious or vulnerable routers, thus making them susceptible to malicious router based attacks (e.g., correlation attacks). In this paper, we propose a novel social network based trust model to help anonymity networks circumvent malicious routers and obtain secure anonymity. In particular, we design an input independent fuzzy model to determine trust relationships between friends based on qualitative and quantitative social attributes, both of which can be readily obtained from existing social networks. Moreover, we design an algorithm for propagating trust over an anonymity network. We integrate these two elements in STor, a novel social network based Tor. We have implemented STor by modifying the Tor's source code and conducted experiments on PlanetLab to evaluate the effectiveness of STor. Both simulation and PlanetLab experiment results have demonstrated that STor can achieve secure anonymity by establishing trust-based circuits in a distributed way. Although the design of STor is based on Tor network, the social network based trust model can be adopted by other anonymity networks.
A (directed) network of people connected by ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. We develop a framework of trust propagation schemes, each of which may be appropriate in certain circumstances, and evaluate the schemes on a large trust network consisting of 800K trust scores expressed among 130K people. We show that a small number of expressed trusts distrust per individual allows us to predict trust between any two people in the system with high accuracy. Our work appears to be the first to incorporate distrust in a computational trust propagation setting.  Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends. Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust. Social networks let the people find and know other people and benefit form their information. Semantic Web standard ontologies support social network sites for making use of other social networks information and hence help their expansion and unification, making them a huge social network. As social networks are public virtual social places much information may exist in them that may not be trustworthy to all. A mechanism in needed to rate coming news, reviews and opinions about a definite subject from users, according to each user preference. There should be a feature for users to specify how much they trust a friend and a mechanism to infer the trust from one user to another that is not directly a friend of the user so that a recommender site can benefit from these trust ratings for showing trustworthy information to each user from her or his point of view from not only her or his directly trusted friends but also the other indirectly trusted users. This work suggests using fuzzy linguistic terms to specify trust to other users and proposes an algorithm for inferring trust from a person to another person that may be not directly connected in the trust graph of a social network. The algorithm is implemented and compared to an algorithm that let the users to specify their trust with a number in a definite range. While according to the imprecise nature of the trust concept writing and reading a linguistic expression for trust is much more natural than a number for users, the results show that the algorithm offers more precise information than the previously used algorithm especially when contradictory beliefs should be composed and also when a more precise inference is potentially possible in searching deeper paths. As the trust graphs and inference are viewed abstractly, they can be well employed in other multi agent systems. People generate information or get it from the others. When one gets information from the others it is important to get it from trusted ones. Each individual in a society can get the information he needs form his trusted friends but there are also many other people in the society that he or she indirectly trusts and can benefit from their information. The idea of benefiting from the indirectly trusted people can well be employed in social networks where finding trusted people can be automated. There should be a feature for users to specify how much they trust a friend and a mechanism to infer the trust in the society trust graph from one user to another that is not directly a friend of the user so that a recommender site can benefit from these inferred trust ratings for showing trustworthy information to each user from her or his point of view from not only her or his directly trusted friends but also the other indirectly trusted users. A problem that is faced in inference in such a large network is contradictory information. This work suggests using fuzzy linguistic terms to specify trust to other users and proposes an algorithm for inferring trust from a person to another person that may be not directly connected in the trust graph of a social network. The algorithm is implemented and compared to the previous one that models trust as numbers in a range. While according to the imprecise nature of trust concept, writing and reading a linguistic expression for trust is much more natural than a number for users, the fuzzy composing strategy performs better than the previous algorithm in integrating conflicting beliefs and conveying the conflict in the inferred result. Privacy -- the protection of information from unauthorized disclosure -- is increasingly scarce on the Internet. The lack of privacy is particularly true for popular peer-to-peer data sharing applications such as BitTorrent where user behavior is easily monitored by third parties. Anonymizing overlays such as Tor and Freenet can improve user privacy, but only at a cost of substantially reduced performance. Most users are caught in the middle, unwilling to sacrifice either privacy or performance. In this paper, we explore a new design point in this tradeoff between privacy and performance. We describe the design and implementation of a new P2P data sharing protocol, called OneSwarm, that provides users much better privacy than BitTorrent and much better performance than Tor or Freenet. A key aspect of the OneSwarm design is that users have explicit configurable control over the amount of trust they place in peers and in the sharing model for their data: the same data can be shared publicly, anonymously, or with access control, with both trusted and untrusted peers. OneSwarm's novel lookup and transfer techniques yield a median factor of 3.4 improvement in download times relative to Tor and a factor of 6.9 improvement relative to Freenet. OneSwarm is publicly available and has been downloaded by hundreds of thousands of users since its release.  The use of previous direct interactions is probably the best way to calculate a reputation but, unfortunately this information is not always available. This is especially true in large multi-agent systems where interaction is scarce. In this paper we present a reputation system that takes advantage, among other things, of social relations between agents to overcome this problem.