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Machine unlearning has become a promising solution for fulfilling the "right to be forgotten", under which individuals can request the deletion of their data from machine learning models.
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2020
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2021
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2022
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N. G. Marchant, B. I. Rubinstein, and S. Alfeld, “Hard to forget: Poisoning attacks on certified machine unlearning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 7, 2022, pp. 7691–7700
2022
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N. Carlini, M. Jagielski, C. Zhang, N. Papernot, A. Terzis, and F. Tramer, “The privacy onion effect: Memorization is relative,” Advances in Neural Information Processing Systems , vol. 35, pp. 13 263–13 276, 2022
2022
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J. Z. Di, J. Douglas, J. Acharya, G. Kamath, and A. Sekhari, “Hidden poison: Machine unlearning enables camouflaged poisoning attacks,” in NeurIPS ML Safety Workshop , 2022
2022
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2022
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D. Zhong, H. Sun, J. Xu, N. Gong, and W. H. Wang, “Understanding disparate effects of membership inference attacks and their countermeasures,” in Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security , 2022, pp. 959–974
2022
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2023
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2023
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H. Hu, S. Wang, J. Chang, H. Zhong, R. Sun, S. Hao, H. Zhu, and M. Xue, “A duty to forget, a right to be assured? exposing vulnerabilities in machine unlearning services,” in 31th Annual Network and Distributed System Security Symposium, NDSS 2024 . The Internet Society, 2024
2024
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