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Lookup NU author(s): Yizhou Shen, Dr Carlton Shepherd, Dr Mujeeb AhmedORCiD
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2025 IEEE. All rights reserved.Malware in the Internet of Things (IoT) is prone to contaminating various IoT end-points through network communication and information transfer, leading to surreptitious privacy leakage and data theft. The existing privacy-preserving approaches including data masking, anonymization, and differential privacy always lack the consideration of strategic interactions among rational agents. Inspired by Bayesian games, we model incomplete stochastic games between IoT end-points and edge nodes in edge intelligence (EI)-enabled IoT systems to conduct probability analysis for predicting and defending privacy leakage caused by malware infection. It is notable that the posterior probability is defined based on the Bayes' rule to reflect the statistical inference of incomplete privacy leakage information. Such a method can intrinsically characterize the actual situations of IoT end-points. Further, we propose a novel privacy preservation optimization approach named Bayesian advantage actor critic (BA2C) for the practical implementation of optimization decision in EI-enabled IoT privacy-preserving systems. Eventually, we conduct experimental simulations to understand the most effective parameters in decision-making among the successful detection rate, successful infection rate, and false alarm rate. We also compare traditional algorithms and validate the efficacy of the proposed approach.
Author(s): Shen Y, Shepherd C, Ahmed CM, Shen S, Yu S
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Mobile Computing
Year: 2025
Pages: epub ahead of print
Online publication date: 03/03/2025
Acceptance date: 02/04/2018
ISSN (print): 1536-1233
ISSN (electronic): 1558-0660
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/TMC.2025.3546910
DOI: 10.1109/TMC.2025.3546910
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