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

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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 a air. 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.