Reputation aggregation in peer-to-peer network using differential gossip algorithm

In a peer-to-peer system, a node should estimate reputation of other peers not only on the basis of its own interaction, but also on the basis of experience of other nodes. Reputation aggregation mechanism implements strategy for achieving this. Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. This paper proposes a reputation aggregation algorithm that uses a variant of gossip algorithm called differential gossip. In this paper, estimate of reputation is considered to be having two parts, one common component which is same with every node, and the other one is the information received from immediate neighbours based on the neighbours' direct interaction with the node. Theoretical analysis and numerical results show that differential gossip is fast and requires lesser amount of resources. The reputation computed using the proposed algorithm also shows a good amount of immunity to the collusion.