Sequential Manipulation Against Rank Aggregation: Theory and Algorithm
Ke Ma, Qianqian Xu, Jinshan Zeng, Wei Liu, Xiaochun Cao, Yingfei Sun,, Qingming Huang

TL;DR
This paper investigates the vulnerabilities of rank aggregation systems to sequential manipulation attacks, proposing game-theoretic models and algorithms that demonstrate how adversaries can effectively disrupt ranking processes.
Contribution
It introduces a novel game-theoretic framework for sequential manipulation of rank aggregation, along with algorithms that optimize attack strategies under uncertainty.
Findings
Equilibrium analysis shows vulnerability of sampling algorithms to manipulation.
Proposed policies are asymptotically optimal with complete knowledge.
Empirical results confirm effective manipulation of rank aggregation outcomes.
Abstract
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…
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