TL;DR
This paper investigates the vulnerability of rank aggregation algorithms to targeted adversarial attacks, modeling the interaction as a game and demonstrating how an attacker can manipulate rankings even with incomplete information.
Contribution
It introduces a game-theoretic framework for targeted attacks on rank aggregation methods and proposes procedures for adversaries to manipulate rankings with limited information.
Findings
Targeted attacks can successfully alter rankings to favor specific candidates.
Proposed methods work effectively even with incomplete or imperfect feedback.
Adversaries can achieve desired rankings under various information constraints.
Abstract
Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has been paid to the security issue of such algorithms, in contrast to numerous research work on the computational and statistical characteristics. Driven by huge profits, the potential adversary has strong motivation and incentives to manipulate the ranking list. Meanwhile, the intrinsic vulnerability of the rank aggregation methods is not well studied in the literature. To fully understand the possible risks, we focus on the purposeful adversary who desires to designate the aggregated results by modifying the pairwise data in this paper. From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim. To…
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