Reputation Aggregation in Peer-to-Peer Network Using Differential Gossip Algorithm

TL;DR

This paper introduces a differential gossip-based reputation aggregation algorithm for peer-to-peer networks that is faster, resource-efficient, and more resistant to collusion, with verified performance on large power law networks.

Contribution

It proposes a novel differential gossip algorithm for reputation aggregation that accounts for node-specific reputations and demonstrates improved efficiency and collusion resistance.

Findings

The algorithm is faster and uses fewer resources than traditional methods.

It provides independent reputation estimates for each node.

The method shows strong immunity to collusion in power law networks.

Abstract

Reputation aggregation in peer to peer networks is generally a very time and resource consuming process. Moreover, most of the methods consider that a node will have same reputation with all the nodes in the network, which is not true. 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 information received from immediate neighbours based on the neighbours' direct interaction with the node. The differential gossip is fast and requires less amount of resources. This mechanism allows computation of independent reputation value by a node, of every other node in the network, for each node. The differential gossip trust has been investigated for a power law network formed using preferential attachment \emph{(PA)} Model. The reputation computed using differential gossip trust shows good amount of immunity to the collusion. We have verified the performance of the algorithm on the power law networks of different sizes ranging from 100 nodes to 50,000 nodes.