When Machine Learning Meets Privacy

Liu, B; Ding, M; Shaham, S; Rahayu, W; Farokhi, F; Lin, Z

When Machine Learning Meets Privacy

Liu, B

Ding, M Shaham, S Rahayu, W Farokhi, F Lin, Z

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Computing Surveys, 2021, 54, (2), pp. 1-36
Issue Date:: 2021-03

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Field	Value	Language
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Ding, M
dc.contributor.author	Shaham, S
dc.contributor.author	Rahayu, W
dc.contributor.author	Farokhi, F
dc.contributor.author	Lin, Z
dc.date.accessioned	2021-03-08T23:48:02Z
dc.date.available	2021-04-01T18:01:37Z
dc.date.issued	2021-03
dc.identifier.citation	ACM Computing Surveys, 2021, 54, (2), pp. 1-36
dc.identifier.issn	0360-0300
dc.identifier.issn	1557-7341
dc.identifier.uri	http://hdl.handle.net/10453/146941
dc.description.abstract	<jats:p>The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.</jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/ARC LP180101150
dc.relation.ispartof	ACM Computing Surveys
dc.relation.isbasedon	10.1145/3436755
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	When Machine Learning Meets Privacy
dc.type	Journal Article
utslib.citation.volume	54
utslib.for	08 Information and Computing Sciences
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 Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-03-08T23:47:53Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	54
utslib.citation.issue	2

Abstract:

The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.

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http://hdl.handle.net/10453/146941