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

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3 Author(s)
Runfang Zhou ; Microsoft Corp., Mountain View, CA ; Kai Hwang ; Min Cai

In peer-to-peer (P2P) networks, reputation aggregation and ranking are the most time-consuming and space-demanding operations. This paper proposes a new gossip protocol for fast score aggregation. We developed a Bloom filter architecture for efficient score ranking. These techniques do not require any secure hashing or fast lookup mechanism, thus are applicable to both unstructured and structured P2P networks. We report the design principles and performance results of a simulated GossipTrust reputation system. Randomized gossiping with effective use of power nodes enables light-weight aggregation and fast dissemination of global scores in O(log2 n) time steps, where n is the P2P 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(n log2 n) messages for n nodes. Bloom filters demand at most 512 KB memory per node for a 10,000-node network. We evaluate the performance of GossipTrust with distributed 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 applications.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:20 ,  Issue: 9 )