When Machine Learning Meets Privacy in 6G: A Survey

Yuanyuan Sun, Jiajia Liu, Jiadai Wang, Yurui Cao, Nei Kato

Research output: Contribution to journalReview articlepeer-review

100 Citations (Scopus)


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.

Original languageEnglish
Article number9146540
Pages (from-to)2694-2724
Number of pages31
JournalIEEE Communications Surveys and Tutorials
Issue number4
Publication statusPublished - 2020 Oct 1


  • 6G
  • communication
  • double-edged sword
  • machine learning
  • Privacy
  • protection
  • violation


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