A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation is the Fixed Point of Adversarial...

Area of Science:

• Computer Science
• Data Science
• Game Theory

Background:

• Rank aggregation using pairwise comparisons is widely applied in various domains like elections and recommendations.
• Existing research primarily focuses on computational and statistical aspects, neglecting the security vulnerabilities of these algorithms.
• Adversarial manipulation of ranking data poses a significant risk due to potential financial or strategic gains.

Purpose of the Study:

• To investigate the security risks associated with rank aggregation methods by analyzing purposeful adversarial attacks.
• To develop and evaluate methods for adversaries to manipulate pairwise comparison data to achieve a desired aggregated ranking.
• To understand the vulnerabilities of rank aggregation algorithms from a dynamical system and game-theoretic perspective.

Main Methods:

• Formulated the adversary-victim interaction as a game-theoretic framework with two continuous operators and established Nash equilibrium.
• Developed specific attack procedures targeting HodgeRank and RankCentrality algorithms by modifying pairwise comparison data.
• Analyzed attack strategies considering scenarios with complete, incomplete information, and imperfect feedback.

Main Results:

• Proved that adversaries with complete information can force rank aggregation algorithms to produce a target ranking.
• Demonstrated the effectiveness of proposed attack strategies through simulations and real-world data experiments.
• Showcased that the manipulated ranking list's leading candidate can be successfully designated by the adversary.

Conclusions:

• Rank aggregation methods are susceptible to targeted attacks, posing significant security risks.
• The developed game-theoretic approach provides a robust framework for understanding and executing such attacks.
• Further research is needed to develop robust defense mechanisms against these identified rank manipulation vulnerabilities.