Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.

翻译：受元宇宙与边缘计算技术的重大进展驱动，车辆边缘元宇宙有望颠覆智能交通系统的现有范式。作为车辆元宇宙用户高度计算机化的化身，部署在边缘服务器中的车辆孪生可为其用户在整个行程中提供有价值的元宇宙服务，以提升驾驶安全性与乘车满意度。为保持不间断的元宇宙体验，车辆孪生需随车辆移动在边缘服务器间进行迁移。这将引发车辆边缘元宇宙动态通信中的隐私泄露问题。为应对这些问题并保护位置隐私，车辆元宇宙用户与车辆孪生均可利用假名作为临时标识符，在物理空间与虚拟空间中实现匿名通信。然而，现有假名管理方法难以满足车辆边缘元宇宙中的大规模假名需求，从而显著降低隐私保护性能。为此，我们提出一种跨元宇宙赋能的双重假名管理框架。通过采用跨链技术增强假名的管理效率与数据安全性，并设计隐私水平评估指标，结合多智能体深度强化学习方法获取最优假名生成策略。数值结果表明，所提方案兼具高效率与成本效益，展现了其在车辆边缘元宇宙中的广阔应用前景。