Page 255 - 《软件学报》2025年第4期
P. 255
李梓童 等: 机器遗忘综述 1661
Event: ACM, 2020. 363–375. [doi: 10.1145/3372297.3417880]
[4] Shokri R, Stronati M, Song CZ, Shmatikov V. Membership inference attacks against machine learning models. In: Proc. of the 2017 IEEE
Symp. on Security and Privacy. San Jose: IEEE, 2017. 3–18. [doi: 10.1109/SP.2017.41]
[5] Newman AL. What the “right to be forgotten” means for privacy in a digital age. Science, 2015, 347(6221): 507–508. [doi: 10.1126/
science.aaa4603]
[6] General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China, Standardization
Administration. GB/T 35273-2020 Information security technology —Personal information security specification. Beijing: Standards
Press of China, 2018 (in Chinese). https://std.samr.gov.cn/gb/search/gbDetailed?id=A0280129495AEBB4E05397BE0A0AB6FE
[7] European Commission. Regulation of the European Parliament and of the Council on the protection of individuals with regard to the
processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection
Regulation). 2012. https://gdpr.eu/article-17-right-to-be-forgotten/
[8] Kwak C, Lee J, Lee H. Forming a dimension of digital human rights: Research agenda for the right to be forgotten. In: Proc. of the 50th
Hawaii Int’l Conf. on System Sciences. Hilton Waikoloa Village: IEEE, 2017. 982–989.
[9] Cao YZ, Yang JF. Towards making systems forget with machine unlearning. In: Proc. of the 2015 IEEE Symp. on Security and Privacy.
San Jose: IEEE, 2015. 463–480. [doi: 10.1109/SP.2015.35]
Engineering, 2023, 35(5): 4646–4667. [doi: 10.1109/TKDE.2022.3148237]
[10] Sharir O, Peleg B, Shoham Y. The cost of training NLP models: A concise overview. arXiv:2004.08900, 2020.
[11] Guo C, Goldstein T, Hannun A, van der Maaten L. Certified data removal from machine learning models. In: Proc. of the 37th Int’l Conf.
on Machine Learning. 2020. 3832–3842.
[12] Liu Y, Fan MY, Chen C, Liu XM, Ma Z, Wang L, Ma JF. Backdoor defense with machine unlearning. In: Proc. of the 2022 IEEE Conf.
on Computer Communications (INFOCOM 2022). London: IEEE, 2022. 280–289. [doi: 10.1109/INFOCOM48880.2022.9796974]
[13] Cao YZ, Yu AF, Adat A, Stahl E, Merwine J, Yang JF. Efficient repair of polluted machine learning systems via causal unlearning. In:
Proc. of the 2018 Asia Conf. on Computer and Communications Security. Incheon: ACM, 2018. 735–747. [doi: 10.1145/3196494.
3196517]
[14] Wang BL, Yao YS, Shan S, Li HY, Viswanath B, Zheng HT, Zhao BY. Neural cleanse: Identifying and mitigating backdoor attacks in
neural networks. In: Proc. of the 2019 IEEE Symp. on Security and Privacy. San Francisco: IEEE, 2019. 707–723. [doi: 10.1109/SP.2019.
00031]
[15] Nguyen TT, Huynh TT, Nguyen PL, Liew AWC, Yin HZ, Nguyen QVH. A survey of machine unlearning. arXiv:2209.02299, 2022.
[16] Xu J, Wu ZH, Wang C, Jia XH. Machine unlearning: Solutions and challenges. IEEE Trans. on Emerging Topics in Computational
Intelligence, 2024, 8(3): 2150–2168. [doi: 10.1109/TETCI.2024.3379240]
[17] Zhang HB, Nakamura T, Isohara T, Sakurai K. A review on machine unlearning. SN Computer Science, 2023, 4(4): 337. [doi: 10.1007/
s42979-023-01767-4]
[18] Xu H, Zhu TQ, Zhang LF, Zhou WL, Yu PS. Machine unlearning: A survey. ACM Computing Surveys, 2023, 56(1): 9. [doi: 10.1145/
3603620]
[19] Liu X, Tsaftaris SA. Have you forgotten? A method to assess if machine learning models have forgotten data. In: Proc. of the 23rd Int’l
Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020). Lima: Springer, 2020. 95–105. [doi: 10.1007/
978-3-030-59710-8_10]
[20] Golatkar A, Achille A, Soatto S. Eternal sunshine of the spotless net: Selective forgetting in deep networks. In: Proc. of the 2020
IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020. 9301–9309. [doi: 10.1109/CVPR42600.2020.00932]
[21] Thudi A, Deza G, Chandrasekaran V, Papernot N. Unrolling SGD: Understanding factors influencing machine unlearning. In: Proc. of the
7th IEEE European Symp. on Security and Privacy (EuroS&P). Genoa: IEEE, 2022. 303–319. [doi: 10.1109/EuroSP53844.2022.00027]
[22] Chai CL, Wang JY, Luo YY, Niu ZP, Li GL. Data management for machine learning: A survey. IEEE Trans. on Knowledge and Data
[23] Liu B, Liu Q, Stone P. Continual learning and private unlearning. arXiv:2203.12817, 2022.
[24] Brophy J, Lowd D. Machine unlearning for random forests. In: Proc. of the 38th Int’l Conf. on Machine Learning. 2021. 1092–1104.
[25] Schelter S, Grafberger S, Dunning T. HedgeCut: Maintaining randomised trees for low-latency machine unlearning. In: Proc. of the 2021
Int’l Conf. on Management of Data. Virtual Event: ACM, 2021. 1545–1557. [doi: 10.1145/3448016.3457239]
[26] Bourtoule L, Chandrasekaran V, Choquette-Choo CA, Jia HR, Travers A, Zhang BW, Lie D, Papernot N. Machine unlearning. In: Proc.
of the 2021 IEEE Symp. on Security and Privacy. San Francisco: IEEE, 2021. 141–159. [doi: 10.1109/SP40001.2021.00019]
[27] Felps DL, Schwickerath AD, Williams JD, Vuong TN, Briggs A, Hunt M, Sakmar E, Saranchak DD, Shumaker T. Class clown: Data
redaction in machine unlearning at enterprise scale. arXiv:2012.04699, 2020.