GossipTrust for Fast Reputation Aggregation in Peer-to-Peer Networks

Runfang Zhou, Kai Hwang, Min Cai
2008 IEEE Transactions on Knowledge and Data Engineering  
In peer-to-peer (P2P) networks, reputation aggregation and peer ranking are the most time-consuming and spacedemanding operations. This paper proposes a gossip-based reputation system (GossipTrust) for fast aggregation of global reputation scores. It leverages a Bloom filter based scheme for efficient score ranking. GossipTrust does not require any secure hashing or fast lookup mechanism, thus is applicable to both unstructured and structured P2P networks. Randomized gossiping with effective
more » ... g with effective use of power nodes enables fast aggregation and fast dissemination of global scores in O(log 2 n) time steps, where n is the network size. The gossip-based protocol is designed to tolerate dynamic peer joining and departure, as well as to avoid possible peer collusions. The scheme has a considerably low gossiping message overhead, i.e. O(nlog 2 n) messages for n nodes. Bloom filters reduce the memory overhead per node to 512 KB for a 10,000-node network. We evaluate the performance of GossipTrust with both P2P file-sharing and parameter-sweeping applications. The simulation results demonstrate that GossipTrust has small aggregation time, low memory demand, and high ranking accuracy. These results suggest promising advantages of using the GossipTrust system for trusted P2P computing.
doi:10.1109/tkde.2008.48 fatcat:rknbcgdl2jay5pacp5lx4gcxna