Interactive Trimming against Evasive Online Data Manipulation Attacks: A Game-Theoretic Approach

• URL: http://arxiv.org/abs/2403.10313v1
• Date: Fri, 15 Mar 2024 13:59:05 GMT
• Title: Interactive Trimming against Evasive Online Data Manipulation Attacks: A Game-Theoretic Approach
• Authors: Yue Fu, Qingqing Ye, Rong Du, Haibo Hu,
• Abstract summary: Malicious data poisoning attacks can disrupt machine learning processes and lead to severe consequences. To mitigate these attacks, distance-based defenses, such as trimming, have been proposed. We present an interactive game-theoretical model to defend online data manipulation attacks using the trimming strategy.
• Score: 10.822843258077997
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: With the exponential growth of data and its crucial impact on our lives and decision-making, the integrity of data has become a significant concern. Malicious data poisoning attacks, where false values are injected into the data, can disrupt machine learning processes and lead to severe consequences. To mitigate these attacks, distance-based defenses, such as trimming, have been proposed, but they can be easily evaded by white-box attackers. The evasiveness and effectiveness of poisoning attack strategies are two sides of the same coin, making game theory a promising approach. However, existing game-theoretical models often overlook the complexities of online data poisoning attacks, where strategies must adapt to the dynamic process of data collection. In this paper, we present an interactive game-theoretical model to defend online data manipulation attacks using the trimming strategy. Our model accommodates a complete strategy space, making it applicable to strong evasive and colluding adversaries. Leveraging the principle of least action and the Euler-Lagrange equation from theoretical physics, we derive an analytical model for the game-theoretic process. To demonstrate its practical usage, we present a case study in a privacy-preserving data collection system under local differential privacy where a non-deterministic utility function is adopted. Two strategies are devised from this analytical model, namely, Tit-for-tat and Elastic. We conduct extensive experiments on real-world datasets, which showcase the effectiveness and accuracy of these two strategies.

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