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Cited by 455Privacy-Preserving Technologies in DataPrivacy, Security, and Data ProtectionCryptography and Data Security

When Machine Learning Meets Privacy: A Survey and Outlook

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Liu, Bo, Ding, Ming, Shaham, Sina, Rahayu, Wenny, Farokhi, Farhad, Lin, Zihuai·2022·ACM Computing Surveys
DOI 10.1145/3436755
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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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  • Machine learning can violate users' expectations of privacy.

    “Ways in which machine learning can violate users' expectations of privacy have been extensively studied [58]”

    From Episodic Memory in AI Agents Poses Risks that Should be Studied and Mitigated (2025) · cites this paper as [58]

    Section 5.1 describes multiple studied ways ML can expose private information, including re-identification and inference attacks. But the Introduction says research has “only scratched the surface,” so the paper does not support the unqualified claim that these ways have been extensively studied. Page numbers are unavailable in our copy. (LLM, conf=0.82)

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Cited by

Sorted by citations

Authors
Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., & Lin, Z.
Affiliations
  • University of Technology Sydney (AU)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
  • The University of Sydney (AU)
  • La Trobe University (AU)
  • The University of Melbourne (AU)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2021-03-05
Volume
54
Issue
2
Pages
1-36
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3436755
OpenAlex
W3135347465
Cited by
455
References
12
Citations by year
2026: 61 · 2025: 108 · 2024: 126 · 2023: 95 · 2022: 43 · 2021: 20 · 2020: 2
FWCI
25.10
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Computing Surveys (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion)
  • UTS ePRESS (University of Technology Sydney) (submittedVersion, other-oa)
Funders
Australian Research Council (LP180101150)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
Keywords
privacy preservation, machine learning privacy, private machine learning, privacy attacks, deep learning, data privacy

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