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

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

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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.

Mobile edge crowdsensingDynamic privacy measurementPersonalized privacy thresholdPrivacy protectionReinforcement learning

Renwan Bi、Mingfeng Zhao、Zuobin Ying、Youliang Tian、Jinbo Xiong

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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

Faculty of Data Science,City University of Macau,999078,Macau,China

State Key Laboratory of Public Big Data,College of Computer Science and Technology,Guizhou University,Guiyang,550025,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaGuangxi Key Laboratory of Trusted SoftwareScience and Technology Major Support Program of Guizhou ProvinceScience and Technology Program of Guizhou ProvinceProject of Highlevel Innovative Talents of Guizhou ProvinceOpen Research Fund of Key Laboratory of Cryptography of Zhejiang Province

U1905211618720886207210961872090U1804263KX202042201830012019109820206008ZCL21015

2024

10.1016/j.dcan.2022.07.013

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(2)
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