Sequential Manipulation Against Rank Aggregation: Theory and Algorithm Article Swipe
Ke Ma
,
Qianqian Xu
,
Jinshan Zeng
,
Wei Liu
,
Xiaochun Cao
,
Yingfei Sun
,
Qingming Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tpami.2024.3416710
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tpami.2024.3416710
Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tpami.2024.3416710
- OA Status
- green
- Cited By
- 5
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399800979
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399800979Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tpami.2024.3416710Digital Object Identifier
- Title
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Sequential Manipulation Against Rank Aggregation: Theory and AlgorithmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-19Full publication date if available
- Authors
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Ke Ma, Qianqian Xu, Jinshan Zeng, Wei Liu, Xiaochun Cao, Yingfei Sun, Qingming HuangList of authors in order
- Landing page
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https://doi.org/10.1109/tpami.2024.3416710Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.01916Direct OA link when available
- Concepts
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Computer science, Algorithm, Artificial intelligence, Rank (graph theory), Pattern recognition (psychology), Mathematics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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