A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT

• Published in: IEEE transactions on industrial informatics Vol. 16; no. 6; pp. 4231 - 4241
• Main Authors: Xiong, Jinbo, Ma, Rong, Chen, Lei, Tian, Youliang, Li, Qi, Liu, Ximeng, Yao, Zhiqiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer simulation; Data collection; Data encryption; Data management; Data privacy; Digitization; Encryption; Game theory; Industrial applications; industrial Internet of Things (IoT); Internet of Things; mobile crowdsensing; personalized privacy protection; Privacy; privacy measurement; Sensors; Task analysis
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2948068

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent data collection and processing paradigm of the industrial Internet of Things, has provided a promising opportunity to construct powerful industrial systems and provide industrial services. The existing unified privacy strategy for all sensing data results in excessive or insufficient protection and low quality of crowdsensing services (QoCS) in MCS. To tackle this issue, in this article we propose a personalized privacy protection (PERIO) framework based on game theory and data encryption. Initially, we design a personalized privacy measurement algorithm to calculate users' privacy level, which is then combined with game theory to construct a rational uploading strategy. Furthermore, we propose a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness. Theoretical analysis and ample simulations with real trajectory dataset indicate that the PERIO scheme is effective and makes a reasonable balance between retaining high QoCS and privacy.