Information theoretical analysis of unfair rating attacks under subjectivity
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 ratin...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/154571 |
| _version_ | 1811684679538966528 |
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| author | Wang, Dongxia Muller, Tim Zhang, Jie Liu, Yang |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Wang, Dongxia Muller, Tim Zhang, Jie Liu, Yang |
| author_sort | Wang, Dongxia |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:32:28Z |
| format | Journal Article |
| id | ntu-10356/154571 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:32:28Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1545712021-12-28T07:41:14Z Information theoretical analysis of unfair rating attacks under subjectivity Wang, Dongxia Muller, Tim Zhang, Jie Liu, Yang School of Computer Science and Engineering Engineering::Computer science and engineering Unfair Rating Attacks Worst-Case Attacks 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. 2021-12-28T07:41:14Z 2021-12-28T07:41:14Z 2020 Journal Article 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. https://dx.doi.org/10.1109/TIFS.2019.2929678 1556-6013 https://hdl.handle.net/10356/154571 10.1109/TIFS.2019.2929678 2-s2.0-85069930291 15 816 828 en IEEE Transactions on Information Forensics and Security © 2019 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Unfair Rating Attacks Worst-Case Attacks Wang, Dongxia Muller, Tim Zhang, Jie Liu, Yang Information theoretical analysis of unfair rating attacks under subjectivity |
| title | Information theoretical analysis of unfair rating attacks under subjectivity |
| title_full | Information theoretical analysis of unfair rating attacks under subjectivity |
| title_fullStr | Information theoretical analysis of unfair rating attacks under subjectivity |
| title_full_unstemmed | Information theoretical analysis of unfair rating attacks under subjectivity |
| title_short | Information theoretical analysis of unfair rating attacks under subjectivity |
| title_sort | information theoretical analysis of unfair rating attacks under subjectivity |
| topic | Engineering::Computer science and engineering Unfair Rating Attacks Worst-Case Attacks |
| url | https://hdl.handle.net/10356/154571 |
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