Learn to Unlearn: Insights Into Machine Unlearning

Qu, Y; Yuan, X; Ding, M; Ni, W; Rakotoarivelo, T; Smith, D

Learn to Unlearn: Insights Into Machine Unlearning

Qu, Y Yuan, X

Ding, M Ni, W

Rakotoarivelo, T Smith, D

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Publisher:: IEEE COMPUTER SOC
Publication Type:: Journal Article
Citation:: Computer, 2024, 57, (3), pp. 79-90
Issue Date:: 2024-03-01

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Field	Value	Language
dc.contributor.author	Qu, Y
dc.contributor.author	Yuan, X https://orcid.org/0000-0002-9167-1613
dc.contributor.author	Ding, M
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Rakotoarivelo, T
dc.contributor.author	Smith, D
dc.date.accessioned	2025-01-15T04:36:32Z
dc.date.available	2025-01-15T04:36:32Z
dc.date.issued	2024-03-01
dc.identifier.citation	Computer, 2024, 57, (3), pp. 79-90
dc.identifier.issn	0018-9162
dc.identifier.issn	1558-0814
dc.identifier.uri	http://hdl.handle.net/10453/183613
dc.description.abstract	This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We highlight emerging challenges and prospective research directions, aiming to provide valuable resources for integrating privacy, equity, and resilience into machine learning systems and help them learn to unlearn.
dc.language	English
dc.publisher	IEEE COMPUTER SOC
dc.relation.ispartof	Computer
dc.relation.isbasedon	10.1109/MC.2023.3333319
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Software Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Learn to Unlearn: Insights Into Machine Unlearning
dc.type	Journal Article
utslib.citation.volume	57
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-15T04:36:31Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	57
utslib.citation.issue	3

Abstract:

This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We highlight emerging challenges and prospective research directions, aiming to provide valuable resources for integrating privacy, equity, and resilience into machine learning systems and help them learn to unlearn.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/183613