A Tunable Mechanism for Identifying Trusted Nodes in Large Scale Distributed Networks

TL;DR

This paper introduces a tunable, randomized protocol for efficiently identifying trusted nodes in large distributed networks, significantly reducing overhead while maintaining high accuracy in trust estimation.

Contribution

The paper presents a novel randomized mechanism that efficiently locates trusted nodes with adjustable overhead, outperforming exhaustive trust estimation methods like TrustWebRank.

Findings

Identifies trusted nodes with 45% exploration of total nodes

Reduces trust estimation overhead by 90%

Trustworthiness measures differ by only 0.6% from TrustWebRank

Abstract

In this paper, we propose a simple randomized protocol for identifying trusted nodes based on personalized trust in large scale distributed networks. The problem of identifying trusted nodes, based on personalized trust, in a large network setting stems from the huge computation and message overhead involved in exhaustively calculating and propagating the trust estimates by the remote nodes. However, in any practical scenario, nodes generally communicate with a small subset of nodes and thus exhaustively estimating the trust of all the nodes can lead to huge resource consumption. In contrast, our mechanism can be tuned to locate a desired subset of trusted nodes, based on the allowable overhead, with respect to a particular user. The mechanism is based on a simple exchange of random walk messages and nodes counting the number of times they are being hit by random walkers of nodes in their neighborhood. Simulation results to analyze the effectiveness of the algorithm show that using the proposed algorithm, nodes identify the top trusted nodes in the network with a very high probability by exploring only around 45% of the total nodes, and in turn generates nearly 90% less overhead as compared to an exhaustive trust estimation mechanism, named TrustWebRank. Finally, we provide a measure of the global trustworthiness of a node; simulation results indicate that the measures generated using our mechanism differ by only around 0.6% as compared to TrustWebRank.