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

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

Digital Communications and Networks (Apr 2024)

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

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

Affiliations

Renwan Bi: 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
Mingfeng Zhao: 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
Zuobin Ying: Faculty of Data Science, City University of Macau, 999078, Macau, China
Youliang Tian: State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China; Corresponding author.
Jinbo Xiong: 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; Corresponding author. Fujian Provincial Key Laboratory of Network Security and Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350117, China.

Journal volume & issue: Vol. 10, no. 2
pp. 380 – 388

Abstract

Read online

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) algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds. Finally, theoretical 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.

Published in Digital Communications and Networks

ISSN: 2352-8648 (Online)
Publisher: KeAi Communications Co., Ltd.
Country of publisher: China
LCC subjects: Technology: Technology (General): Industrial engineering. Management engineering: Information technology
Website: https://www.keaipublishing.com/en/journals/digital-communications-and-networks/

About the journal

Abstract

Keywords