构建进化稳定性和信号游戏模型,以保护物联网中的隐私
Yu Li1, Huamin Liu2, Lei Lu3
1Public Basics Department, Ganzhou Polytechnic, Ganzhou, 341000, China.
Scientific reports
|July 1, 2025
概括
本研究介绍了一种新的物联网-PSGDL模型,用于增强物联网 (IoT) 隐私保护. 它使用游戏理论和深度学习实现了98.25%的有效性来对抗不断变化的威胁.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 游戏理论 游戏理论
背景情况:
- 物联网 (IoT) 环境由于相互连接的设备和数据交换而带来了重大隐私挑战.
- 现有的隐私保护策略往往难以适应物联网威胁的动态和复杂性质.
- 信息不对称性和隐私泄露风险是物联网安全的关键因素.
研究的目的:
- 开发和评估物联网环境的优化隐私保护策略.
- 用游戏理论分析用户,设备,网络运营商和攻击者之间的互动.
- 提高物联网中的隐私保护机制的有效性和响应性.
主要方法:
- 游戏模型的构建,整合进化游戏理论和信号游戏机制.
- 开发物联网隐私保护的多阶段信号游戏和深度学习模型 (IoT-PSGDL).
- 在CIC物联网数据集上进行实验性评估,评估隐私保护有效性,持久性,延迟和安全性等指标.
主要成果:
- 拟议的物联网-PSGDL模型实现了98.25%的隐私保护效率,优于其他模型.
- 在隐私保护持久性 (97.56%),通信延迟 (54.12ms) 和数据存储安全性 (96.75%) 中表现出强的表现.
- 确定数据加密是非常有效的策略,成功率为96.72%,隐私泄露概率为2.14%.
结论:
- 物联网-PSGDL模型为物联网隐私保护提供了一种高效且动态优化的解决方案.
- 这项研究为解决复杂物联网生态系统中各种隐私威胁提供了强大的框架.
- 这些发现强调了适应性策略和特定技术的重要性,例如数据加密,以确保物联网环境的安全.
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