Abstract
The choice of trustworthy interaction partners is one of the key factors for successful transactions in online communities. To choose the most trustworthy sellers to interact with, buyers rely on trust and reputation models. Therefore, online systems must be able to accurately assess peers’ trustworthiness. The Beta distribution function provides a sound mathematical basis for combining feedback and deriving users’ trustworthiness. But the Beta reputation system suffers from many forms of cheating behavior such as the proliferation of unfair positive ratings, leading a poor service provider to build a good reputation, and the proliferation of unfair negative feedback, leading a good service provider to end up with a bad reputation. In this paper, we propose a new and coherent method for computing users’ trustworthiness by combining the Beta trustworthiness expectation function with the credibility function. This novel combination mechanism mitigates the impact of unfair ratings. In comparison with Bayesian trust model, we quantitatively show that our approach provides significantly more accurate estimation of peers’ trustworthiness through feedback gathered from multiple sources. Furthermore, we propose an extension of Bayesian trustworthiness expectation function by introducing the initial trust propensity to allow assessing individuals’ initial trust.
Highlights
Social networks and e-commerce platforms are developed to enable social actors to share information and develop lucrative activities. These platforms have been successful in terms of security, but their openness and their ability to accommodate a large number of players make them vulnerable due to the proportionate number of malicious users that is associated
As in [1], we argue that certificates alone are not enough and that we must take into account the behavior of all participants in opportunistic networks such as social networks and e-commerce platforms
We argue that trust systems that rely on the Bayesian standard trust model are flawed in several ways
Summary
Social networks and e-commerce platforms are developed to enable social actors to share information and develop lucrative activities. These platforms have been successful in terms of security, but their openness and their ability to accommodate a large number of players make them vulnerable due to the proportionate number of malicious users that is associated. In order to reduce the impact of unfair ratings (both positive and negative) faced by Bayesian reputation systems, a novel trust evaluation model is proposed for quantifying users’ trustworthiness.
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