An AI-Enabled Three-Party Game Framework for Guaranteed Data Privacy in Mobile Edge Crowdsensing of IoT

The mobile crowdsensing (MCS) technology with a large number of Internet of Things (IoT) devices provides an economic and efficient solution to participation in coordinated large-scale sensing tasks. Edge computing powers MCS to form the mobile edge crowdsensing (MECS) framework. Privacy disclosure...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2021-02, Vol.17 (2), p.922-933
Main Authors: Xiong, Jinbo, Zhao, Mingfeng, Bhuiyan, Md Zakirul Alam, Chen, Lei, Tian, Youliang
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Artificial intelligence
Data models
Data privacy
Edge computing
Game theory
Games
Informatics
Internet of Things
Machine learning
mobile edge crowdsensing (MECS)
Nash equilibrium (NE)
Privacy
privacy protection
Sensors
Task analysis
three-party game (TG) model
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Summary:The mobile crowdsensing (MCS) technology with a large number of Internet of Things (IoT) devices provides an economic and efficient solution to participation in coordinated large-scale sensing tasks. Edge computing powers MCS to form the mobile edge crowdsensing (MECS) framework. Privacy disclosure of sensing data in multiple stages is a significant challenge in the MECS. To tackle this issue, combining machine learning with game theory, in this article, we propose an artificial intelligence (AI)-enabled three-party game (ATG) framework for guaranteed data privacy in the MECS of IoT. Specifically, based on the random forest classifier and the k-anonymity algorithm, we propose a classification-anonymity model that effectively guarantees the privacy of sensitive data. Moreover, we construct a three-party game model for analyzing the data privacy leakage in different phases in the MECS. Finally, we conduct numerical and theoretical analyses and ample simulations. The results indicate that the ATG framework is effective and efficient, and better suited to the MECS of IoT.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2957130