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

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.

;Comment: This manuscript is accepted by ICDE '24