{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T04:01:07Z","timestamp":1778904067058,"version":"3.51.4"},"reference-count":54,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T00:00:00Z","timestamp":1720051200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102035"],"award-info":[{"award-number":["62102035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022ZD0115901"],"award-info":[{"award-number":["2022ZD0115901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["High-Confidence Computing"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.hcc.2024.100254","type":"journal-article","created":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T01:25:12Z","timestamp":1720229112000},"page":"100254","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"title":["An overview of machine unlearning"],"prefix":"10.1016","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8025-8041","authenticated-orcid":false,"given":"Chunxiao","family":"Li","sequence":"first","affiliation":[]},{"given":"Haipeng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jiankang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Shuxuan","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Fangming","family":"Jing","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Guo","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.hcc.2024.100254_b1","series-title":"2015 IEEE Symposium on Security and Privacy","first-page":"463","article-title":"Towards making systems forget with machine unlearning","author":"Cao","year":"2015"},{"key":"10.1016\/j.hcc.2024.100254_b2","first-page":"7691","article-title":"Hard to forget: Poisoning attacks on certified machine unlearning","volume":"vol. 36","author":"Marchant","year":"2022"},{"issue":"4","key":"10.1016\/j.hcc.2024.100254_b3","article-title":"Data leakage detection using cloud computing","volume":"6","author":"Singh","year":"2017","journal-title":"Int. J. Eng. Comput. Sci."},{"key":"10.1016\/j.hcc.2024.100254_b4","doi-asserted-by":"crossref","unstructured":"R. Wang, Y.F. Li, X. Wang, H. Tang, X. Zhou, Learning your identity and disease from research papers: Information leaks in genome wide association study, in: Proceedings of the 16th ACM Conference on Computer and Communications Security, 2009, pp. 534\u2013544.","DOI":"10.1145\/1653662.1653726"},{"key":"10.1016\/j.hcc.2024.100254_b5","series-title":"2021 IEEE Symposium on Security and Privacy","first-page":"141","article-title":"Machine unlearning","author":"Bourtoule","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b6","doi-asserted-by":"crossref","unstructured":"Q.P. Nguyen, R. Oikawa, D.M. Divakaran, M.C. Chan, B.K.H. Low, Markov chain Monte Carlo-based machine unlearning: Unlearning what needs to be forgotten, in: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, 2022, pp. 351\u2013363.","DOI":"10.1145\/3488932.3517406"},{"key":"10.1016\/j.hcc.2024.100254_b7","first-page":"16025","article-title":"Variational Bayesian unlearning","volume":"33","author":"Nguyen","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.hcc.2024.100254_b8","doi-asserted-by":"crossref","unstructured":"M. Du, Z. Chen, C. Liu, R. Oak, D. Song, Lifelong anomaly detection through unlearning, in: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019, pp. 1283\u20131297.","DOI":"10.1145\/3319535.3363226"},{"key":"10.1016\/j.hcc.2024.100254_b9","doi-asserted-by":"crossref","unstructured":"A. Golatkar, A. Achille, S. Soatto, Eternal sunshine of the spotless net: Selective forgetting in deep networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9304\u20139312.","DOI":"10.1109\/CVPR42600.2020.00932"},{"key":"10.1016\/j.hcc.2024.100254_b10","series-title":"Annual International Conference on the Theory and Applications of Cryptographic Techniques","first-page":"373","article-title":"Formalizing data deletion in the context of the right to be forgotten","author":"Garg","year":"2020"},{"key":"10.1016\/j.hcc.2024.100254_b11","article-title":"Making ai forget you: Data deletion in machine learning","volume":"vol. 32","author":"Ginart","year":"2019"},{"key":"10.1016\/j.hcc.2024.100254_b12","first-page":"18075","article-title":"Remember what you want to forget: Algorithms for machine unlearning","volume":"34","author":"Sekhari","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.hcc.2024.100254_b13","series-title":"Certified data removal from machine learning models","author":"Guo","year":"2019"},{"key":"10.1016\/j.hcc.2024.100254_b14","series-title":"Algorithmic Learning Theory","first-page":"931","article-title":"Descent-to-delete: Gradient-based methods for machine unlearning","author":"Neel","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b15","series-title":"International Conference on Machine Learning","first-page":"1092","article-title":"Machine unlearning for random forests","author":"Brophy","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b16","series-title":"2022 IEEE 7th European Symposium on Security and Privacy","first-page":"303","article-title":"Unrolling sgd: Understanding factors influencing machine unlearning","author":"Thudi","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b17","series-title":"2017 IEEE Symposium on Security and Privacy","first-page":"3","article-title":"Membership inference attacks against machine learning models","author":"Shokri","year":"2017"},{"key":"10.1016\/j.hcc.2024.100254_b18","series-title":"Machine unlearning of features and labels","author":"Warnecke","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b19","series-title":"Efficient attribute unlearning: Towards selective removal of input attributes from feature representations","author":"Guo","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00349-y","article-title":"Boosting methods for multi-class imbalanced data classification: An experimental review","volume":"7","author":"Tanha","year":"2020","journal-title":"J. Big Data"},{"key":"10.1016\/j.hcc.2024.100254_b21","article-title":"Fast yet effective machine unlearning","author":"Tarun","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.hcc.2024.100254_b22","series-title":"2018 31st SIBGRAPI Conference on Graphics, Patterns and Images","first-page":"471","article-title":"Deep face recognition: A survey","author":"Masi","year":"2018"},{"key":"10.1016\/j.hcc.2024.100254_b23","series-title":"Towards probabilistic verification of machine unlearning","author":"Sommer","year":"2020"},{"key":"10.1016\/j.hcc.2024.100254_b24","article-title":"Verifying in the dark: Verifiable machine unlearning by using invisible backdoor triggers","author":"Guo","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.hcc.2024.100254_b25","series-title":"Deepobliviate: a powerful charm for erasing data residual memory in deep neural networks","author":"He","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b26","unstructured":"A. Thudi, H. Jia, I. Shumailov, N. Papernot, On the necessity of auditable algorithmic definitions for machine unlearning, in: 31st USENIX Security Symposium, USENIX Security 22, 2022, pp. 4007\u20134022."},{"key":"10.1016\/j.hcc.2024.100254_b27","doi-asserted-by":"crossref","unstructured":"M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, Y. Zhang, When machine unlearning jeopardizes privacy, in: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021, pp. 896\u2013911.","DOI":"10.1145\/3460120.3484756"},{"key":"10.1016\/j.hcc.2024.100254_b28","series-title":"Bounding membership inference","author":"Thudi","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b29","series-title":"2019 IEEE Symposium on Security and Privacy","first-page":"707","article-title":"Neural cleanse: Identifying and mitigating backdoor attacks in neural networks","author":"Wang","year":"2019"},{"key":"10.1016\/j.hcc.2024.100254_b30","first-page":"268","article-title":"Athena: Probabilistic verification of machine unlearning","volume":"3","author":"Sommer","year":"2022","journal-title":"Proc. Priv. Enhanc. Technol."},{"key":"10.1016\/j.hcc.2024.100254_b31","series-title":"Machine unlearning via GAN","author":"Chen","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b32","doi-asserted-by":"crossref","unstructured":"J. Wang, S. Guo, X. Xie, H. Qi, Federated unlearning via class-discriminative pruning, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 622\u2013632.","DOI":"10.1145\/3485447.3512222"},{"issue":"9","key":"10.1016\/j.hcc.2024.100254_b33","doi-asserted-by":"crossref","first-page":"3203","DOI":"10.1007\/s10994-022-06178-9","article-title":"Machine unlearning: Linear filtration for logit-based classifiers","volume":"111","author":"Baumhauer","year":"2022","journal-title":"Mach. Learn."},{"key":"10.1016\/j.hcc.2024.100254_b34","series-title":"Proceedings of the 24th International Conference on Artificial Intelligence and Statistics","first-page":"2008","article-title":"Approximate data deletion from machine learning models","volume":"vol. 130","author":"Izzo","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"10.1016\/j.hcc.2024.100254_b36","doi-asserted-by":"crossref","unstructured":"S. Schelter, S. Grafberger, T. Dunning, Hedgecut: Maintaining randomised trees for low-latency machine unlearning, in: Proceedings of the 2021 International Conference on Management of Data, 2021, pp. 1545\u20131557.","DOI":"10.1145\/3448016.3457239"},{"key":"10.1016\/j.hcc.2024.100254_b37","series-title":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics","first-page":"1","article-title":"Machine unlearning method based on projection residual","author":"Cao","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b38","doi-asserted-by":"crossref","unstructured":"A. Golatkar, A. Achille, A. Ravichandran, M. Polito, S. Soatto, Mixed-privacy forgetting in deep networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 792\u2013801.","DOI":"10.1109\/CVPR46437.2021.00085"},{"key":"10.1016\/j.hcc.2024.100254_b39","series-title":"Unlearnable examples: Making personal data unexploitable","author":"Huang","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b40","first-page":"8675","article-title":"Puma: Performance unchanged model augmentation for training data removal","volume":"vol. 36","author":"Wu","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b41","series-title":"2021 IEEE\/ACM 29th International Symposium on Quality of Service","first-page":"1","article-title":"Federaser: Enabling efficient client-level data removal from federated learning models","author":"Liu","year":"2021"},{"key":"10.1016\/j.hcc.2024.100254_b42","series-title":"Federated unlearning with knowledge distillation","author":"Wu","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b43","series-title":"IEEE INFOCOM 2022-IEEE Conference on Computer Communications","first-page":"1749","article-title":"The right to be forgotten in federated learning: An efficient realization with rapid retraining","author":"Liu","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b44","article-title":"A multi-batch L-BFGS method for machine learning","volume":"29","author":"Berahas","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.hcc.2024.100254_b45","series-title":"International Conference on Machine Learning","first-page":"620","article-title":"A progressive batching L-BFGS method for machine learning","author":"Bollapragada","year":"2018"},{"key":"10.1016\/j.hcc.2024.100254_b46","series-title":"Conference on Lifelong Learning Agents","first-page":"243","article-title":"Continual learning and private unlearning","author":"Liu","year":"2022"},{"issue":"13","key":"10.1016\/j.hcc.2024.100254_b47","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.hcc.2024.100254_b48","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual lifelong learning with neural networks: A review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."},{"key":"10.1016\/j.hcc.2024.100254_b49","series-title":"A survey of machine unlearning","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b50","doi-asserted-by":"crossref","DOI":"10.1109\/TIFS.2023.3265506","article-title":"Zero-shot machine unlearning","author":"Chundawat","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.hcc.2024.100254_b51","series-title":"Verifi: Towards verifiable federated unlearning","author":"Gao","year":"2022"},{"key":"10.1016\/j.hcc.2024.100254_b52","first-page":"20673","article-title":"Learning from failure: De-biasing classifier from biased classifier","volume":"33","author":"Nam","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"44035","key":"10.1016\/j.hcc.2024.100254_b53","first-page":"46992","article-title":"Amnesia-a selection of machine learning models that can forget user data very fast","volume":"8364","author":"Schelter","year":"2020","journal-title":"suicide"},{"key":"10.1016\/j.hcc.2024.100254_b54","doi-asserted-by":"crossref","unstructured":"X. Hu, K. Tang, C. Miao, X.-S. Hua, H. Zhang, Distilling causal effect of data in class-incremental learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 3957\u20133966.","DOI":"10.1109\/CVPR46437.2021.00395"}],"container-title":["High-Confidence Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667295224000576?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667295224000576?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T20:10:37Z","timestamp":1751400637000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667295224000576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["S2667295224000576"],"URL":"https:\/\/doi.org\/10.1016\/j.hcc.2024.100254","relation":{},"ISSN":["2667-2952"],"issn-type":[{"value":"2667-2952","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An overview of machine unlearning","name":"articletitle","label":"Article Title"},{"value":"High-Confidence Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.hcc.2024.100254","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V. on behalf of Shandong University.","name":"copyright","label":"Copyright"}],"article-number":"100254"}}