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Title: Information theoretical analysis of unfair rating attacks under subjectivity
Authors: Wang, Dongxia
Muller, Tim
Zhang, Jie
Liu, Yang
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Wang, D., Muller, T., Zhang, J. & Liu, Y. (2020). Information theoretical analysis of unfair rating attacks under subjectivity. IEEE Transactions On Information Forensics and Security, 15, 816-828.
Journal: IEEE Transactions on Information Forensics and Security
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, which does not provide a complete picture of the attacks. We provide the first formal study that addresses all attack behavior that is possible within a given system. We propose a probabilistic modeling of rating behavior, 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.
ISSN: 1556-6013
DOI: 10.1109/TIFS.2019.2929678
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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