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

Abstract

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.

22 pages, 8 figures, accepted by IEEE Internet of Things Journal

References in corpus (6)

• Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures
• Challenges and Opportunities in Securing the Industrial Internet of Things
• Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees
• Privacy Preserving Face Recognition Utilizing Differential Privacy
• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
• Correlated Differential Privacy: Feature Selection in Machine Learning

Cited by in corpus (6)

• Security and privacy for 6G: A survey on prospective technologies and challenges
• FedMood: Federated Learning on Mobile Health Data for Mood Detection
• Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach
• Using Decentralized Aggregation for Federated Learning with Differential Privacy
• Differential Privacy in Cognitive Radio Networks: A Comprehensive Survey
• A Human-Centered Privacy Approach (HCP) to AI