Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
<|reference_start|>Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse: 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.<|reference_end|>
@article{kang2024blockchain-based,
    title={Blockchain-based Pseudonym Management for Vehicle Twin Migrations in
  Vehicular Edge Metaverse},
    author={Jiawen Kang, Xiaofeng Luo, Jiangtian Nie, Tianhao Wu, Haibo Zhou,
  Yonghua Wang, Dusit Niyato, Shiwen Mao, Shengli Xie},
    journal={arXiv preprint arXiv:2403.15285},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15285},
    primaryClass={cs.NI cs.CR cs.HC cs.LG}
}