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.

中文翻译：

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当机器学习遇到 6G 中的隐私时：一项调查

快速发展的人工智能（AI）技术、快速增长的网络流量以及新兴的智能应用（如自动驾驶、虚拟现实等）迫切需要一种新的、更快、更可靠和灵活的网络形式。此时，工业界和学术界的研究人员已将注意力转向第六代（6G）通信网络。在6G愿景中，利用机器学习（ML）技术（人工智能最重要的分支）的各种智能应用场景将带来丰富的异构连接，以及海量的信息存储和操作。当机器学习遇到 6G 时，新的机遇将伴随着众多隐私挑战而出现。一方面，安全的机器学习结构，或者机器学习的正确应用，可以在6G中保护隐私。另一方面，ML 可能会受到攻击或滥用，从而导致隐私侵犯。值得注意的是，6G和ML的联盟在很多情况下也可能是一把双刃剑，而不是绝对侵犯或保护隐私。因此，本文基于大量现有有意义的工作，旨在对 6G 中的 ML 和隐私进行全面调查，以期进一步推动 6G 和隐私保护技术的发展。