期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 6, 页码 4231-4241出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2948068
关键词
Sensors; Data privacy; Privacy; Task analysis; Encryption; Game theory; Game theory; industrial Internet of Things (IoT); mobile crowdsensing; personalized privacy protection; privacy measurement
类别
资金
- National Natural Science Foundation of China [61872088, U1804263, 61872090, 61702105, 61772008, 61662009]
- Key Lab of Information Network Security, Ministry of Public Security [C18602]
- Science and Technology Major Support Program of Guizhou Province [20183001]
- Natural Science Foundation of Fujian Province [2019J01276]
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.
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