Semantic Scholar Open Access 2020 172 sitasi

A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT

Jinbo Xiong Rong Ma Lei Chen Youliang Tian Qi Li +2 lainnya

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (7)

Jinbo Xiong

Rong Ma

Lei Chen

Youliang Tian

Qi Li

Ximeng Liu

Zhiqiang Yao

Format Sitasi

APA MLA BibTeX

Xiong, J., Ma, R., Chen, L., Tian, Y., Li, Q., Liu, X. et al. (2020). A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT. https://doi.org/10.1109/TII.2019.2948068

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 172×
Sumber Database: Semantic Scholar
DOI: 10.1109/TII.2019.2948068
Akses: Open Access ✓