Liu, H., Zhu, T., Zhang, L., & Xiong, P. (2021). Game-Theoretic Machine Unlearning: Mitigating Extra Privacy Leakage. Journal of LaTeX Class Files, 14(8).
This paper addresses the challenge of achieving effective machine unlearning while mitigating the risk of privacy leakage inherent in removing data and its influence from trained machine learning models.
The authors propose a novel game-theoretic machine unlearning algorithm that models the unlearning process as a Stackelberg game between two modules: an unlearning module and a privacy module. The unlearning module aims to optimize model parameters to remove the influence of the unlearned data while maintaining model performance. The privacy module, acting as the follower, seeks to minimize the attacker's advantage in inferring membership information from the unlearned model.
The game-theoretic machine unlearning algorithm provides a promising solution for balancing the trade-off between effective data removal and privacy preservation in machine learning models.
This research contributes to the growing field of machine unlearning by addressing the critical challenge of privacy leakage. The proposed algorithm offers a practical and efficient solution for organizations seeking to comply with data privacy regulations while maintaining the utility of their machine learning models.
The paper focuses on classification tasks and specific membership inference attacks. Future research could explore the algorithm's applicability to other machine learning tasks and privacy attack models. Additionally, investigating the impact of different game-theoretic models and strategies on unlearning performance and privacy preservation could be beneficial.
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by Hengzhu Liu,... at arxiv.org 11-07-2024
https://arxiv.org/pdf/2411.03914.pdfDeeper Inquiries