Trust information is dependent to the Structure of p2p network

A trust model for P2P networks is presented, in which a peer can develop a trust network in its proximity. A peer can isolate malicious peers around itself as it develops trust relationships with good peers. Two context of trust, service and recommendation contexts, are defined to measure capabilities of peers in providing services and giving recommendations. Interactions and recommendations are considered with satisfaction, weight, and fading effect parameters. A recommendation contains the recommender’s own experience, information from its acquaintances, and level of confidence in the recommendation. These parameters provided us a better assessment of trustworthiness. Individual, collaborative, and pseudonym changing attackers are studied in the experiments. Damage of collaboration and pseudospoofing is dependent to attack behavior. Although recommendations are important in hypocritical and oscillatory attackers, pseudospoofers, and collaborators, they are less useful in naive and discriminatory attackers. SORT mitigated both service and recommendation-based attacks in most experiments. The following assumptions. Peers are equal in computational power and responsibility. There are no privileged, centralized, or trusted peers to manage trust relationships. Peers occasionally leave and join the network. A peer provides services and uses services of others. For simplicity of discussion, one type of interaction is considered in the service context, i.e., file download. PEER-TO-PEER (P2P) systems rely on collaboration of peers to accomplish tasks. Ease of performing malicious activity is a threat for security of P2P systems. Creating long-term trust relationships among peers can provide a more secure environment by reducing risk and uncertainty in future P2P interactions. Establishing trust in an unknown entity is difficult in such a malicious environment. Trust is a social concept and hard to measure with numerical values. Metrics are needed to represent trust in computational models. Classifying peers as either trustworthy or untrustworthy is not sufficient in most cases. Metrics should have precision so peers can be ranked according to trustworthiness. Interactions and feedbacks of peers provide information to measure trust among peers. Interactions with a peer provide certain information about the peer but feedbacks might contain deceptive information. This makes assessment of trustworthiness a challenge. In the presence of an authority, a central server is a preferred way to store and manage trust information, e.g., eBay. The central server securely stores trust information and defines trust metrics. Since there is no central server in most P2P systems, peers organize themselves to store and manage trust information about each other. Management of trust information is dependent to the structure of P2P network.