Spreading evidence models for trust propagation and aggregation in peer-to-peer networks.

SUMMARY Trust model plays an important role in ensuring the security of interactions in peer-to-peer (P2P) systems where a peer's trust evaluation depends on the interaction experience of its own and recommendation information from other peers. However, current trust models have limitations in solving not only the issues of time efficiency of direct interaction information but also the reliability and inconsistency of recommendation information. In this paper, we propose a Dempster-Shafer evidence theory based trust model (ETTM) for P2P systems. The primary goal of ETTM is to be able to address information uncertainty and conflicting recommendation problems in a reputation-based P2P environment. To make D-S theory fits into P2P applications, we creatively revise the combination rules and achieve greatly improved results. To further improve the accuracy and performance, ETTM filters out noisy referrals if they are not compatible with most other evidence. In addition, a feedback-based probabilistic searching algorithm is proposed to find the referrals with improved searching success rate and lowered network traffic. Experimental results show ETTM has a clear advantage in aggregating recommendation information. Moreover, ETTM is more robust and can generate a higher successful transaction rate than some other existing frameworks. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]