Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges

【Author】 Jiang, Bin; Li, Jianqiang; Yue, Guanghui; Song, Houbing

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this article, we conduct a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT. We first review related papers on IIoT and privacy protection, respectively. Then, we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up research on industrial differential privacy.

【Keywords】Industrial Internet of Things; Differential privacy; Privacy; Security; Publishing; Industries; Big Data; Deep models; differential privacy; industrial IoT (IIoT); privacy disclosure; privacy metrics

【发表时间】2021 44743

【收录时间】2022-01-02

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