Journal ArticleDOI
A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT
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TLDR
A personalized privacy protection (PERIO) framework based on game theory and data encryption, which is then combined with game theory to construct a rational uploading strategy and a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness.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.read more
Citations
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Federated Learning in Smart City Sensing: Challenges and Opportunities.
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An AI-Enabled Three-Party Game Framework for Guaranteed Data Privacy in Mobile Edge Crowdsensing of IoT
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Lightweight Searchable Encryption Protocol for Industrial Internet of Things
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Blockchain-based eHealth system for auditable EHRs manipulation in cloud environments
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References
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Journal Article
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Proceedings ArticleDOI
Efficient aggregation of encrypted data in wireless sensor networks
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