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35th Int’l Conf. on Neural Information Processing Systems. Online: Curran Associates Inc., 2021. 18075–18086.
[83] Chen M, Zhang ZK, Wang TH, Backes M, Humbert M, Zhang Y. Graph unlearning. In: Proc. of the 2022 ACM SIGSAC Conf. on
Computer and Communications Security. Los Angeles: ACM, 2022. 499–513. [doi: 10.1145/3548606.3559352]
[84] Zhu XR, Li GY, Hu W. 2023. Heterogeneous federated knowledge graph embedding learning and unlearning. In: Proc. of the 2023 ACM
Web Conf. Austin: ACM, 2023. 2444–2454. [doi: 10.1145/3543507.3583305]
[85] Li YT, Wang CH, Cheng G. Online forgetting process for linear regression models. In: Proc. of the 24th Int’l Conf. on Artificial
Intelligence and Statistics. 2021. 217–225.
[86] Mirzasoleiman B, Karbasi A, Krause A. Deletion-robust submodular maximization: Data summarization with “the right to be forgotten”.
In: Proc. of the 34th Int’l Conf. on Machine Learning. 2017. 2449–2458.
[87] Yu C, Jeoung S, Kasi A, Yu PF, Ji H. Unlearning bias in language models by partitioning gradients. In: Findings of the Association for
Computational Linguistics. Toronto: ACL, 2023. 6032–6048. [doi: 10.18653/v1/2023.findings-acl.375]
[88] Yao YS, Xu XJ, Liu Y. Large language model unlearning. arXiv:2310.10683, 2024.
[89] Chen JA, Yang DY. Unlearn what you want to forget: Efficient unlearning for LLMs. arXiv:2310.20150, 2023.
[90] Liu Z, Kalinli O. Forgetting private textual sequences in language models via leave-one-out ensemble. arXiv:2309.16082, 2023.
[91] Ni SW, Chen DW, Li CM, Hu XP, Xu RF, Yang M. Forgetting before learning: Utilizing parametric arithmetic for knowledge updating
in large language models. arXiv:2311.08011, 2024.
孟小峰(1964－), 男, 博士, 教授, 博士生导师,
[92] Liu GY, Ma XQ, Yang Y, Wang C, Liu JC. FedEraser: Enabling efficient client-level data removal from federated learning models. In:
Proc. of the 29th IEEE/ACM Int’l Symp. on Quality of Service. Tokyo: IEEE, 2021. 1–10. [doi: 10.1109/IWQOS52092.2021.9521274]
[93] Liu Y, Xu L, Yuan XL, Wang C, Li B. The right to be forgotten in federated learning: An efficient realization with rapid retraining. In:
Proc. of the IEEE INFOCOM 2022 —IEEE Conf. on Computer Communications. London: IEEE, 2022. 1749–1758. [doi: 10.1109/
INFOCOM48880.2022.9796721]
[94] Liu Y, Ma Z, Liu XM, Ma JF. Learn to forget: User-level memorization elimination in federated learning. arXiv:2003.10933, 2021.
[95] Gong J, Kang J, Simeone O, Kassab R. Forget-SVGD: Particle-based Bayesian federated unlearning. In: Proc. of the 2022 IEEE Data
Science and Learning Workshop. Singapore: IEEE, 2022. 1–6. [doi: 10.1109/DSLW53931.2022.9820602]
[96] Wang JX, Guo S, Xie X, Qi H. Federated unlearning via class-discriminative pruning. In: Proc. of the 2022 ACM Web Conf. Virtual
Event: ACM, 2022. 622–632. [doi: 10.1145/3485447.3512222]
[97] Che TS, Zhou Y, Zhang ZJ, Lyu LJ, Liu J, Yan D, Dou DJ, Huan J. Fast federated machine unlearning with nonlinear functional theory.
In: Proc. of the 40th Int’l Conf. on Machine Learning. 2023. 4241–4268.
[98] Chen M, Zhang ZK, Wang TH, Backes M, Humbert M, Zhang Y. When machine unlearning jeopardizes privacy. In: Proc. of the 2021
ACM SIGSAC Conf. on Computer and Communications Security. Virtual Event: ACM, 2021. 896–911. [doi: 10.1145/3460120.3484756]

附中文参考文献:
[6] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 35273-2020 信息安全技术 个人信息安全规范. 北
京: 中国标准出版社, 2018. https://std.samr.gov.cn/gb/search/gbDetailed?id=A0280129495AEBB4E05397BE0A0AB6FE

李梓童(1999－), 女, 硕士生, 主要研究领域为隐 王雷霞(1994－), 女, 博士生, 主要研究领域为数
私保护. 据隐私保护.

郝新丽(1995－), 女, 博士生, 主要研究领域为大
CCF 会士, 主要研究领域为云数据管理, 网络数 数据分析.
据管理, 隐私保护.