期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 13, 页码 10430-10451出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3057419
关键词
Industrial Internet of Things; Differential privacy; Privacy; Security; Publishing; Industries; Big Data; Deep models; differential privacy; industrial IoT (IIoT); privacy disclosure; privacy metrics
资金
- China Postdoctoral Science Foundation [2020M680125]
- National Natural Science Foundation of China [U1713212]
- National Science Foundation [1956193]
The development of IoT has brought new changes and IIoT is promoting a new industrial revolution. With more IIoT applications, privacy protection issues are emerging. Differential privacy is used to protect user-terminal privacy, requiring in-depth research.
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.
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