Abstract The Internet of things (IoT) aims to connect everything and everyone around the world to provide diverse applications that improve quality of life. In this technology, the preservation of data privacy plays a crucial role. Recently, many studies have leveraged machine learning (ML) as a strategy to address the privacy issues of IoT including scalability, interoperability, and resource limitation such as computation and energy. In this paper, we aim to review these studies and examine opportunities and concerns related to utilizing data in ML-based solutions for privacy in IoT. We, first, explore and introduce different data sources in IoT and categorize them. Then, we review existing ML-based solutions that are designed and developed to protect privacy in IoT. Finally, we examine the extent to which some data categories have been used with ML-based solutions to preserve privacy and propose other novel opportunities for ML-based solutions to leverage these data sources in the IoT ecosystem.

中文翻译：

---

基于机器学习的物联网隐私保护解决方案调查

摘要 物联网 (IoT) 旨在连接世界各地的一切事物和每个人，以提供提高生活质量的多样化应用。在这项技术中，保护数据隐私起着至关重要的作用。最近，许多研究利用机器学习 (ML) 作为解决物联网隐私问题的策略，包括可扩展性、互操作性以及计算和能源等资源限制。在本文中，我们旨在回顾这些研究，并研究与在基于 ML 的物联网隐私解决方案中利用数据相关的机会和问题。我们首先在物联网中探索和引入不同的数据源并对其进行分类。然后，我们回顾了现有的基于机器学习的解决方案，这些解决方案旨在保护物联网中的隐私。最后，