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 (ML) is 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 paper 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.

中文翻译：

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当机器学习遇到隐私时：调查和展望

新兴的机器学习（例如深度学习）方法已成为推动广泛行业变革的强大动力，例如智能医疗保健，金融技术和监视系统。同时，在基于机器学习的人工智能时代，隐私已成为人们关注的重点。重要的是要注意，机器学习中的隐私保护问题与传统的数据隐私保护中的问题大不相同，因为机器学习既可以成为敌手，也可以成为敌人。当前，保护隐私和机器学习（ML）的工作仍处于起步阶段，因为大多数现有解决方案仅关注机器学习过程中的隐私问题。因此，需要对隐私保护问题和机器学习进行全面研究。本文概述了隐私问题和机器学习解决方案的最新状况。该调查涵盖了隐私和机器学习之间的三类交互：（i）私有机器学习，（ii）机器学习辅助的隐私保护，以及（iii）基于机器学习的隐私攻击和相应的保护方案。审查了每个类别的当前研究进展，并确定了关键挑战。最后，基于对隐私和机器学习领域的深入分析，我们指出了该领域的未来研究方向。（iii）基于机器学习的隐私攻击和相应的保护方案。审查了每个类别的当前研究进展，并确定了关键挑战。最后，基于对隐私和机器学习领域的深入分析，我们指出了该领域的未来研究方向。（iii）基于机器学习的隐私攻击和相应的保护方案。审查了每个类别的当前研究进展，并确定了关键挑战。最后，基于对隐私和机器学习领域的深入分析，我们指出了该领域的未来研究方向。