Gossip-Based Aggregation of Trust in Decentralized Reputation Systems

Yoram Bachrach, Ariel Parnes, Ariel D. Procaccia, and Jeffrey S. Rosenschein. Journal of Autonomous Agents and Multiagent Systems. Volume 19, Number 2, October 2009, pages 153-172.

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Decentralized Reputation Systems have recently emerged as a prominent method of establishing trust among self-interested agents in online environments. A key issue is the efficient aggregation of data in the system; several approaches have been proposed, but they are plagued by major shortcomings.

We put forward a novel, decentralized data management scheme grounded in gossip-based algorithms. Rumor mongering is known to possess algorithmic advantages, and indeed, our framework inherits many of its salient features: scalability, robustness, globality, and simplicity.

We demonstrate that our scheme motivates agents to maintain a sparkling clean reputation, by showing that the higher an agent’s reputation is above the threshold set by her peers, the more transactions she would be able to complete within a certain time unit. We analyze the relation between the amount by which an agent’s average reputation exceeds the threshold and the time required to close a deal. This analysis is carried out both theoretically, and empirically through a simulation system called GossipTrustSim. Finally, we show that our approach is inherently impervious to certain kinds of attacks.

,title={Gossip-Based Aggregation of Trust in Decentralized Reputation
,author={Yoram Bachrach and Ariel Parnes and Ariel D. Procaccia
and Jeffrey S. Rosenschein}
,journal={Journal of Autonomous Agents and Multi-Agent Systems}