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Game-theoretic analytics for privacy preservation in Internet of Things networks: A survey

Lookup NU author(s): Yizhou Shen, Dr Carlton Shepherd, Dr Mujeeb AhmedORCiD


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© 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.

Publication metadata

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

ISSN (print): 0952-1976

ISSN (electronic): 1873-6769

Publisher: Elsevier Ltd


DOI: 10.1016/j.engappai.2024.108449


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