Quantifying robustness of trust systems against collusive unfair rating attacks using information theory

Wang, Dongxia; Muller, Tim; Zhang, Jie; Liu, Yang

Abstract

Unfair rating attacks happen in existing trust and reputation systems, lowering the quality of the systems. There exists a formal model that measures the maximum impact of independent attackers [Wang et al., 2015] - based on information theory. We improve on these results in multiple ways: (1) we alter the methodology to be able to reason about colluding attackers as well, and (2) we extend the method to be able to measure the strength of any attacks (rather than just the strongest attack). Using (1), we identify the strongest collusion attacks, helping construct robust trust system. Using (2), we identify the strength of (classes of) attacks that we found in the literature. Based on this, we help to overcome a shortcoming of current research into collusion-resistance - specific (types of) attacks are used in simulations, disallowing direct comparisons between analyses of systems.

Citation

Wang, D., Muller, T., Zhang, J., & Liu, Y. (2015, July). Quantifying robustness of trust systems against collusive unfair rating attacks using information theory. Presented at International Joint Conferences on Artificial Intelligence, Buenos Aires, Argentina

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International Joint Conferences on Artificial Intelligence
• Start Date: Jul 25, 2015
• End Date: Jul 31, 2015
• Acceptance Date: Apr 16, 2015
• Publication Date: Jul 1, 2015
• Deposit Date: Jan 13, 2020
• Publisher: International Joint Conferences on Artificial Intelligence
• Pages: 111-117
• Book Title: IJCAI'15: Proceedings of the 24th International Conference on Artificial Intelligence
• ISBN: 9781577357384
• Public URL: https://nottingham-repository.worktribe.com/output/2141626
• Publisher URL: https://dl.acm.org/doi/10.5555/2832249.2832265