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A survey of machine learning-based solutions to protect privacy in the Internet of Things

机译:基于机器学习的解决方案调查,以保护内容互联网隐私

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摘要

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.
机译:事物互联网(物联网)旨在连接世界各地的一切和每个人,以提供改善生活质量的多样化应用。在这项技术中,数据隐私的保存起着至关重要的作用。最近,许多研究已经利用机器学习(ML)作为解决物联网隐私问题的策略,包括可扩展性,互操作性和资源限制,如计算和能量。在本文中,我们的目标是审查这些研究,并检查与利用ML的基于ML的数据进行数据,以便在IOT中的隐私相关的机会和疑虑。首先,我们首先探索和引入IOT中的不同数据源并将其分类。然后,我们审查了设计和开发的基于ML的解决方案,以保护IOT中的隐私。最后,我们研究了一些数据类别已被用于基于ML的解决方案的程度,以保护隐私,并提出基于ML的解决方案的其他新机遇,以利用物联网生态系统中的这些数据来源。

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