Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

Renwan Bi ¹Mingfeng Zhao ¹Zuobin Ying ²Youliang Tian ³Jinbo Xiong¹

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作者信息

1. Fujian Provincial Key Laboratory of Network Security and Cryptology,College of Computer and Cyber Security,Fujian Normal University,Fuzhou,350117,China;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,541004,China
2. Faculty of Data Science,City University of Macau,999078,Macau,China
3. State Key Laboratory of Public Big Data,College of Computer Science and Technology,Guizhou University,Guiyang,550025,China
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Abstract

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)al-gorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theo-retical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63％and 23％higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Key words

Mobile edge crowdsensing/Dynamic privacy measurement/Personalized privacy threshold/Privacy protection/Reinforcement learning

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基金项目

National Natural Science Foundation of China(U1905211)

National Natural Science Foundation of China(61872088)

National Natural Science Foundation of China(62072109)

National Natural Science Foundation of China(61872090)

National Natural Science Foundation of China(U1804263)

Guangxi Key Laboratory of Trusted Software(KX202042)

Science and Technology Major Support Program of Guizhou Province(20183001)

Science and Technology Program of Guizhou Province(20191098)

Project of Highlevel Innovative Talents of Guizhou Province(20206008)

Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province(ZCL21015)

出版年

2024

数字通信与网络(英文)

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参考文献量4

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