Cited by 211Privacy-Preserving Technologies in DataPrivacy, Security, and Data ProtectionCybersecurity and Information Systems

When Machine Learning Meets Privacy in 6G: A Survey

Sun, Yuanyuan, Liu, Jiajia, Wang, Jiadai, Cao, Yurui, Kato, Nei·2020·IEEE Communications Surveys & Tutorials

Abstract

The rapid-developing Artificial Intelligence (AI) technology, fast-growing network traffic, and emerging intelligent applications (e.g., autonomous driving, virtual reality, etc.) urgently require a new, faster, more reliable and flexible network form. At this time, researchers in both industry and academia have turned their attention to the sixth generation (6G) communication networks. In the 6G vision, various intelligent application scenarios that utilize Machine Learning (ML) technology (the most important branch of AI) will bring rich heterogeneous connections, as well as massive information storage and operations. When ML meets 6G, new opportunities will emerge along with numerous privacy challenges. On one hand, a secure ML structure, or the correct application of ML, can protect privacy in 6G. On the other hand, ML may be attacked or abused, resulting in privacy violation. It is worth noting that the alliance between 6G and ML may also be a double-edged sword in many cases, rather than absolutely infringe or protect privacy. Therefore, based on lots of existing meaningful works, this paper aims to provide a comprehensive survey of ML and privacy in 6G, with a view to further promoting the development of 6G and privacy protection technologies.

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Crossref · 10.1109/comst.2020.3011561

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201 of 201 references verified0 citation errors · 0 not checkable

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  • Multidevice federated learning systems are vulnerable to membership inference attacks when the adversary is a participant.

    “There are certain shortcomings of federated learning such as multidevice systems being vulnerable to membership inference attacks when the adversary is a participant [128].”

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Authors
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N.
Affiliations
  • Xidian University (CN)
  • Northwestern Polytechnical University (CN)
  • Tohoku University (JP)
Venue
IEEE Communications Surveys & Tutorials
Published
2020
Volume
22
Issue
4
Pages
2694-2724
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
OpenAlex
W3044837284
Cited by
211
References
203
Citations by year
2026: 10 · 2025: 29 · 2024: 50 · 2023: 38 · 2022: 44 · 2021: 33 · 2020: 7
FWCI
11.99
Type
journal article
Language
en
Open access
closed
Locations
IEEE Communications Surveys & Tutorials (publishedVersion)
Funders
  • National Natural Science Foundation of China (61771374, 61771373, 61801360, 61601357)
  • Natural Science Basic Research Program of Shaanxi (2020JC-15, 2020JM-109)
  • Fundamental Research Funds for the Central Universities (3102019PY005, 31020190QD040, 31020200QD010)
  • Special Funds for Central Universities Construction of World-Class Universities (Disciplines) and Special Development Guidance (06390-20GH020114)
  • China 111 Project (B16037)
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 › Computer Networks and Communications › Cybersecurity and Information Systems
Keywords
6G, machine learning, privacy preservation, privacy risks, secure machine learning, intelligent networks, privacy violations, artificial intelligence in communication

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