  The development of Internet of Things (IoT) brings new changes to various
fields. Particularly, industrial Internet of Things (IIoT) is promoting a new
round of industrial revolution. With more applications of IIoT, privacy
protection issues are emerging. Specially, 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 paper, we conduct a comprehensive survey on the
opportunities, applications and challenges of differential privacy in IIoT. We
firstly 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 researches on industrial
differential privacy.
