Recent developments in technologies such as MEC and AI contribute significantly in accelerating the deployment of VCPS. Techniques such as dynamic content caching, efficient resource allocation, and data sharing play a crucial role in enhancing the service quality and user driving experience. Meanwhile, data leakage in VCPS can lead to physical consequences such as endangering passenger safety and privacy, and causing severe property loss for data providers. The increasing volume of data, the dynamic network topology, and the availability of limited resources make data leakage in VCPS an even more challenging problem, especially when it involves multiple users and multiple transmission channels. In this article, we first propose a secure and intelligent architecture for enhancing data privacy. Then we present our new privacy-preserving federated learning mechanism and design a two-phase mitigating scheme consisting of intelligent data transformation and collaborative data leakage detection. Numerical results based on a real-world dataset demonstrate the effectiveness of our proposed scheme and show that our scheme achieves good accuracy, efficiency, and high security.

中文翻译：

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车载网络物理系统中数据隐私保护的联邦学习


MEC和AI等技术的最新发展为加速VCPS的部署做出了巨大贡献。动态内容缓存、高效资源分配、数据共享等技术对于提升服务质量和用户驾驶体验发挥着至关重要的作用。同时，VCPS中的数据泄露可能会导致危及乘客安全和隐私、给数据提供者带来严重财产损失等物理后果。不断增加的数据量、动态的网络拓扑以及有限资源的可用性使得VCPS中的数据泄漏成为更具挑战性的问题，特别是当涉及多个用户和多个传输通道时。在本文中，我们首先提出了一种用于增强数据隐私的安全智能架构。然后，我们提出了新的隐私保护联邦学习机制，并设计了一个由智能数据转换和协作数据泄漏检测组成的两阶段缓解方案。基于真实世界数据集的数值结果证明了我们提出的方案的有效性，并表明我们的方案实现了良好的准确性、效率和高安全性。