Probabilistic prediction of peers' performance in P2P networks

The problem of encouraging trustworthy behavior in P2P online communities by managing peers' reputations has drawn a lot of attention recently. However, most of the proposed solutions exhibit the following two problems: huge implementation overhead and unclear trust related model semantics. This paper shows that a simple probabilistic technique, maximum likelihood estimation namely, can reduce these two problems substantially when employed as the feedback aggregation strategy. We evaluate the technique in three settings relevant for applications of P2P networks and show that it performs well in all of them. Thus, no complex exploration of the feedback is necessary. Instead, simple, intuitive and efficient probabilistic estimation methods suffice.