A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT

Jinbo Xiong, Rong Ma, Lei Chen, Youliang Tian, Qi Li, Ximeng Liu, Zhiqiang Yao

Research output: Contribution to journalArticlepeer-review

188 Scopus citations

Abstract

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.

Original languageEnglish
Article number8873641
Pages (from-to)4231-4241
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume16
Issue number6
DOIs
StatePublished - Jun 2020

Scopus Subject Areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Game theory
  • industrial Internet of Things (IoT)
  • mobile crowdsensing
  • personalized privacy protection
  • privacy measurement

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