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

In peer-to-peer networks, free riding is a major problem. Reputation management systems can be used to overcome this problem. Reputation estimation methods generally do not considers the uncertainties in the inputs. We propose a reputation estimation method using BLUE (Best Linear Unbiased estimator) estimator that consider uncertainties in the input variables. Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. Moreover, these methods consider that reputation of a node is same for every other node in the network, while this is not true. This paper also proposes a reputation aggregation algorithm that uses a variant of gossip algorithm called differential gossip. Differential gossip is fast and requires less amount of resources. This mechanism allows computation of different reputation value of a node for every other 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 the algorithm on power law networks of different sizes ranging from 100 nodes to 50,000 nodes.