Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

Shen, Yizhou, Shen, Shigen, Li, Qi, Zhou, Haiping, Wu, Zongda and Qu, Youyang 2023. Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes. Digital Communications and Networks 9 (4) , pp. 906-919. 10.1016/j.dcan.2022.05.004

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Start Date: 2022-05-11
Publisher:	Elsevier
ISSN:	2352-8648
Date of First Compliant Deposit:	4 September 2023
Date of Acceptance:	10 May 2022
Last Modified:	10 Feb 2024 02:23
URI:	https://orca.cardiff.ac.uk/id/eprint/162195

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