Towards Reliable Reputations for Dynamic Networked Systems

Abstract

A new generation of distributed systems and applications rely on the cooperation of diverse user populations motivated by self-interest. While they can utilize "reputation systems" to reduce selfish behaviors that disrupt or manipulate the network for personal gain, current reputations face a key challenge in large dynamic networks: vulnerability to peer collusion. In this paper, we propose to dramatically improve the accuracy of reputation systems with the use of a statistical metric that measures the "reliability" of a peer's reputation taking into account collusion-like behavior. Trace-driven simulations on P2P network traffic show that our reliability metric drastically improves system performance. We also apply our metric to 18,000 randomly selected eBay user reputation profiles, and surprisingly discover numerous users with collusion-like behaviors worthy of additional investigation.