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Lookup NU author(s): Yizhou Shen, Dr Carlton Shepherd, Dr Mujeeb AhmedORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 Elsevier Ltd. Privacy preservation of the big data generated, deposited, and communicated by smart IoT (Internet of Things) nodes is the major challenge in IoT networks. Anonymization, encryption, and routing protocol constitute the existing prevalent privacy-preserving approaches, most of which have successfully implemented the privacy preservation of data query, data mining and data aggregation. Nevertheless, there has been a gradual switch in the selection of privacy-preserving technology. Predictive game-theoretic analytics for privacy preservation in IoT networks has received significant attention since it can effectively settle the conflicts between attackers and defenders. In this survey, we explain the basics of various games mainly applied for IoT privacy preservation, such as simultaneous game, stochastic game, bargain game, differential game, mean field game, aggregation game, Stackelberg game, signaling game, repeated game, evolutionary game, and cooperative game. We then explore different applications for game theory-based privacy preservation in IoT networks, followed by discussing the differences among the existing solution of privacy-preserving issues using different games under specific IoT scenarios. Moreover, we consider the challenges and outline future research directions. In conclusion, this survey not only presents existing work on applying game theory to preserve privacy in current IoT networks including smart grids, intelligent transportation systems, crowdsensing, edge-based IoT, integrated energy systems, blockchain IoT, Social IoT and Industrial IoT, but it also encourages researches to further dig deeper into rare areas.
Author(s): Shen Y, Shepherd C, Ahmed CM, Shen S, Wu X, Ke W, Yu S
Publication type: Article
Publication status: Published
Journal: Engineering Applications of Artificial Intelligence
Year: 2024
Volume: 133
Issue: Part E
Print publication date: 01/07/2024
Online publication date: 03/05/2024
Acceptance date: 12/04/2024
Date deposited: 07/08/2026
ISSN (print): 0952-1976
ISSN (electronic): 1873-6769
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.engappai.2024.108449
DOI: 10.1016/j.engappai.2024.108449
ePrints DOI: 10.57711/5p0z-hq90
Data Access Statement: No data was used for the research described in the article.
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