Generalized Analysis of Convergence of Absolute Trust in Peer to Peer Networks

TL;DR

This paper provides a generalized analysis of the convergence properties of the Absolute Trust algorithm used for trust aggregation in peer-to-peer networks, addressing issues of malicious behavior and trust stability.

Contribution

It introduces a comprehensive convergence analysis of the Absolute Trust algorithm, enhancing understanding of its reliability in dynamic peer-to-peer environments.

Findings

Convergence conditions for Absolute Trust are established.

The analysis improves trust management robustness against malicious peers.

The results guide better design of trust aggregation algorithms.

Abstract

Open and anonymous nature of peer to peer networks provides an opportunity to malicious peers to behave unpredictably in the network. This leads the lack of trust among the peers. To control the behavior of peers in the network, reputation system can be used. In a reputation system, aggregation of trust is a primary issue. Algorithm for aggregation of trust should be designed such that, it can converge to a certain finite value. Absolute Trust is one of the algorithm, which is used for the aggregation of trust in peer to peer networks. In this letter, we present the generalized analysis of convergence of the Absolute Trust algorithm.