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Trust Estimation and Aggregation in Peer-to-Peer Network Using Differential Gossip Algorithm

In peer-to-peer network, free riding is a big problem. Reputation management systems are used to overcome free riding. Reputation computation methods generally do not consider the uncertainty caused during reputation computation. This paper proposes a reputation estimation method using BLUE (Best Linear Unbiased estimator) estimator that consider all uncertainties. 
Reputation aggregation in peer to peer networks is generally very time and resource consuming affair. Moreover these methods consider that reputation of a node is same for every node in the network, while that is not true. This paper also proposes a reputation aggregation algorithm that uses a variant of gossip algorithm, differential gossip. Differential gossip is fast and requires sizable amount of resources. Moreover it computes different reputation value of anode for every node in the network. We have implemented Differential Gossip Trust for power law network formed using PA Model. The reputation computed using differential gossip trust shows good immunity to collusion. We have verified the performance of algorithm on power law networks of different sizes ranging from 100 nodes to 50,000 nodes.