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物联网中隐私保护的进化稳定性与信号博弈模型构建

Construction of evolutionary stability and signal game model for privacy protection in the internet of things.

作者信息

Li Yu, Liu Huamin, Lu Lei

机构信息

Public Basics Department, Ganzhou Polytechnic, Ganzhou, 341000, China.

Information Engineering College, Ganzhou Polytechnic, Ganzhou, 341000, China.

出版信息

Sci Rep. 2025 Jul 1;15(1):20800. doi: 10.1038/s41598-025-08836-z.

DOI:10.1038/s41598-025-08836-z
PMID:40593151
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12217400/
Abstract

The research focuses on privacy protection in the Internet of Things environment. A model based on evolutionary theory game and signal game mechanism is proposed to analyze and optimize privacy protection strategies. The study introduces evolutionary game theory and signal game mechanism to construct a game model between users, devices, network operators, and attackers. Detailed discussions are conducted on factors such as privacy protection needs, information asymmetry, and privacy leakage risks. The proposed Multi-stage Signal Game and Deep Learning Model for IoT Privacy Protection (IoT-PSGDL) performed the best on privacy protection effectiveness, at 98.25% on the CIC IoT dataset, with a policy update speed of 7.42 updates/second and a system response time of 35.12ms. Compared with other models, the proposed model performed well in multiple metrics, such as privacy protection persistence (97.56%), communication latency (54.12ms), and data storage security (96.75%). In addition, privacy protection strategies such as data encryption performed the best in the experiment, with a privacy protection success rate of 96.72% and the lowest privacy leakage probability of only 2.14%. The significance of the research lies in providing an efficient and dynamically optimized privacy protection strategy that can effectively respond to various privacy threats in complex Internet of Things environments.

摘要

该研究聚焦于物联网环境中的隐私保护。提出了一种基于进化理论博弈和信号博弈机制的模型，用于分析和优化隐私保护策略。该研究引入进化博弈理论和信号博弈机制，构建了用户、设备、网络运营商和攻击者之间的博弈模型。对隐私保护需求、信息不对称和隐私泄露风险等因素进行了详细讨论。所提出的物联网隐私保护多阶段信号博弈与深度学习模型（IoT-PSGDL）在隐私保护有效性方面表现最佳，在CIC物联网数据集上达到了98.25%，策略更新速度为7.42次更新/秒，系统响应时间为35.12毫秒。与其他模型相比，所提出的模型在多个指标上表现良好，如隐私保护持久性（97.56%）、通信延迟（54.12毫秒）和数据存储安全性（96.75%）。此外，数据加密等隐私保护策略在实验中表现最佳，隐私保护成功率为96.72%，隐私泄露概率最低仅为2.14%。该研究的意义在于提供一种高效且动态优化的隐私保护策略，能够有效应对复杂物联网环境中的各种隐私威胁。

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Plant-Mimetic Vertical-Channel Hydrogels for Synergistic Water Purification and Interfacial Water Evaporation.
用于协同水净化和界面水蒸发的植物模拟垂直通道水凝胶。
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