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

Conference item

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.

Authors: Wang, D; Muller, T; Zhang, J; Liu, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: International Joint Conferences on Artificial Intelligence
• Host title: Proceedings of the 24th International Joint Conference on Artificial Intelligence
• Journal: Proceedings of the 24th International Joint Conference on Artificial Intelligence
• Pages: 111-117
• Publication date: 2015-09-25
• Acceptance date: 2015-04-16
• ISBN: 9781577357384
• Keywords: SBTMR