文献类型：期刊文献

英文题名：Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

作者：Shen, Yizhou[1,2];Shen, Shigen[3];Li, Qi[1];Zhou, Haiping[1];Wu, Zongda[1];Qu, Youyang[4]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales;[3]Huzhou Univ, Sch Informat Engn, Huzhou 313000, Zhejiang, Peoples R China;[4]Deakin Univ, Sch Informat Technol, Burwood, Vic 3125, Australia

年份：2023

卷号：9

期号：4

起止页码：906

外文期刊名：DIGITAL COMMUNICATIONS AND NETWORKS

收录：SCI-EXPANDED(收录号：WOS:001075088900001)、、EI(收录号：20241015711764)、Scopus(收录号：2-s2.0-85153035840)、WOS

基金：This work was supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant nos. LZ22F020002 and LY22F020003, National Natural Science Foundation of China under Grant nos. 61772018 and 62002226, and the key project of Humanities and Social Sciences in Colleges and Universities of Zhejiang Province under Grant no. 2021GH017.

语种：英文

外文关键词：Privacy preservation; Internet of things; Evolutionary game; Data sharing; Edge computing

外文摘要：The fast proliferation of edge devices for the Internet of Things (IoT) has led to massive volumes of data explosion. The generated data is collected and shared using edge-based IoT structures at a considerably high frequency. Thus, the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes. To address the identified issue, we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme. In particular, we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes, where IoT devices and edge nodes are two parties of the game. IoT devices may make malicious requests to achieve their goals of stealing privacy. Accordingly, edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed. They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs. Built upon a developed application framework to illustrate the concrete data sharing architecture, a novel algorithm is proposed that can derive the optimal evolutionary learning strategy. Furthermore, we numerically simulate evolutionarily stable strategies, and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme. Therefore, the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.