A Reputation-Based Trust Model Based on Probability and Statistics for P2P Systems

Peer to peer (P2P) technology has been widely used in file-sharing, distributed computing, e-market and information management. One of the fundamental challenges for P2P systems is the ability to manage risks involved in interacting and collaborating with prior unknown and potentially malicious parties. Reputation-based trust management systems can successfully mitigate this risk by deriving the trustworthiness of a certain peer from that peer's behavior history. However, in current trust models employed by the existing P2P systems, the validity of peers' trust valuation is seriously affected by peers' malicious behaviors. For example, there are many strategic cheating and dishonest recommendation. To solve this problem, a novel reputation-based trust model based on probability and statistics for P2P systems is proposed. Referring to subjective trust relationship of sociological theory, the proposed model uses experience-based and recommendation-based trust relationship to compute the trustworthiness of peers. In particular, this model introduces three parameters, namely, experience's time-sensitivity, referee's credibility and recommended information's reliability, and thus it can provide adequate reaction to peers' malicious behaviors. Theoretical analysis and simulation show that the proposed model has advantages in coping with peers' malicious behaviors over the existing P2P reputation systems. It is highly effective in countering malicious peers regarding strategic cheating, dishonest recommendation, and collusion.