Towards truthful feedback in P2P data structures

Peer-to-Peer data structures (P2P data structures) let a large number of anonymous peers share the data-management workload. A common assumption behind such systems is that peers behave cooperatively. But as with many distributed systems where participation is voluntary, and the participants are not clearly observable, unreliable behavior is the dominant strategy. This calls for reputation systems that help peers choose reliable peers to interact with. However, if peers exchange feedback on experiences with other peers, spoof feedback becomes possible, compromising the reputation system. In this paper we propose and evaluate measures against spoof feedback in P2P data structures. While others have investigated mechanisms for truthtelling recently, we are not aware of any studies in P2P environments. The problem is more difficult in our context because detecting unreliable peers is more difficult as well. On the other hand, a peer can observe the utility of feedback obtained from other peers, and our approach takes advantage of this. To assess the effectiveness of our approach, we have conducted extensive analytical and experimental evaluations. As a result, truthful feedback tends to have a much higher weight than spoof feedback, and collaboration attacks are difficult to carry out under our approach.