A two-level solution to fight against dishonest opinions in recommendation-based trust systems

TL;DR

This paper introduces a two-level mechanism to combat dishonest opinions in recommendation-based trust systems, improving robustness against collusion and Sybil attacks by combining self-assessment and resilient aggregation techniques.

Contribution

It presents a novel two-level approach addressing dishonest opinions at both collection and processing stages, enhancing trust system security against collusion and Sybil attacks.

Findings

Outperforms existing models in resisting Sybil attacks.

Effective in identifying dishonest recommenders through self-assessment.

Improves trust accuracy in recommendation systems.

Abstract

In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in which an agent requests recommendations from multiple parties to build trust toward another agent. At the collection level, we propose to allow agents to self-assess the accuracy of their recommendations and autonomously decide on whether they would participate in the recommendation process or not. At the processing level, we propose a recommendations aggregation technique that is resilient to collusion attacks, followed by a credibility update mechanism for the participating agents. The originality of our work stems from its consideration of dishonest opinions at both the collection and processing levels, which allows for better and more persistent protection against dishonest recommenders. Experiments conducted on the Epinions dataset show that our solution yields better performance in protecting the recommendation process against Sybil attacks, in comparison with a competing model that derives the optimal network of advisors based on the agents' trust values.