Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory

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

Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory

Dongxia Wang, Tim Muller, Jie Zhang, Yang Liu

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 111-116.

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.

Dongxia Wang, Tim Muller, Jie Zhang, and Yang Liu. 2015. Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 111–116. https://ijcai.org/proceedings/2015/23

@inproceedings{ijcai2015p23,
  title     = {Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory},
  author    = {Wang, Dongxia and Muller, Tim and Zhang, Jie and Liu, Yang},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-15}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Qiang Yang and Michael Wooldridge},
  pages     = {111--116},
  year      = {2015},
  month     = {7},
  note      = {Main Track},
  url       = {https://ijcai.org/proceedings/2015/23},
}

TY  - CPAPER
TI  - Quantifying Robustness of Trust Systems against Collusive Unfair Rating Attacks Using Information Theory
AU  - Wang, Dongxia
AU  - Muller, Tim
AU  - Zhang, Jie
AU  - Liu, Yang
T2  - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2015
SP  - 111
EP  - 116
UR  - https://ijcai.org/proceedings/2015/23
ER  -