Information Theoretical Analysis of Unfair Rating Attacks under Subjectivity

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

Abstract

Ratings provided by advisors can help an advisee to make decisions, e.g., which seller to select in e-commerce. Unfair rating attacks – where dishonest ratings are provided to mislead the advisee – impact the accuracy of decision making. Current literature focuses on specific classes of unfair rating attacks, but this does not provide a complete picture of the attacks. We provide the first formal study that addresses all attack behaviour that is possible within a given system. We propose a probabilistic modelling of rating behaviour, and apply information theory to quantitatively measure the impact of attacks. In particular, we can identify the attack with the worst impact. In the simple case, honest advisors report the truth straightforwardly, and attackers rate strategically. In real systems, the truth (or an advisor’s view on it) may be subjective, making even honest ratings inaccurate. Although there exist methods to deal with subjective ratings, whether subjectivity influences the effect of unfair rating attacks was an open question. We discover that subjectivity decreases the robustness against attacks.

Citation

Wang, D., Muller, T., Zhang, J., & Liu, Y. (2019). Information Theoretical Analysis of Unfair Rating Attacks under Subjectivity. IEEE Transactions on Information Forensics and Security, 15, 816-828. https://doi.org/10.1109/TIFS.2019.2929678

• Journal Article Type: Article
• Acceptance Date: Jul 2, 2019
• Online Publication Date: Jul 18, 2019
• Publication Date: 2019
• Deposit Date: Jul 8, 2019
• Publicly Available Date: Jul 18, 2019
• Journal: IEEE Transactions on Information Forensics and Security
• Print ISSN: 1556-6013
• Electronic ISSN: 1556-6021
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Pages: 816-828
• DOI: https://doi.org/10.1109/TIFS.2019.2929678
• Keywords: Unfair rating Attacks, Worst-case attacks, Robustness, Subjective rating, Trust systems
• Public URL: https://nottingham-repository.worktribe.com/output/2286193
• Publisher URL: https://ieeexplore.ieee.org/document/8765826
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Jul 8, 2019