BaRMS: A Bayesian Reputation Management approach for P2P systems

Current distributed applications offer a variety of flexible and convenient services through the Internet to users from different geographic locations and also support communications among them. However, security and trust are key concerns in such applications as users in such an environment are unknown to each other. Trust management systems aim to decrease the risks in such applications and protect benign users from malicious users. In this paper, we propose a novel Bayesian Reputation Management System (BaRMS) for Peer-to-Peer (P2P) environments using Bayesian probability and Markov Chain theories. BaRMS handles negative feedbacks and through a case study, we show that this approach is better than the existing EigenTrust framework for P2P systems. Moreover, our simulation results of a P2P file sharing system also show that the proposed algorithm can greatly improve the performance over a system that does not include a trust management service. We show that our proposed Bayesian Reputation Computation Algorithm (BaRCA) performs better than the EigenTrust algorithm.