research-article

When Machine Learning Meets Privacy: A Survey and Outlook

Authors:

Farhad Farokhi,

Zihuai LinAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 2

Article No.: 31, Pages 1 - 36

https://doi.org/10.1145/3436755

Published: 05 March 2021 Publication History

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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When Machine Learning Meets Privacy: A Survey and Outlook
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
  2. Software and application security
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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 2

March 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3450359

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Albert Zomaya
University of Sydney, Australia

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Published: 05 March 2021

Accepted: 01 November 2020

Revised: 01 November 2020

Received: 01 March 2019

Published in CSUR Volume 54, Issue 2

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