Blockchain-based Pseudonym Management for Vehicle Twin Migrations in
  Vehicular Edge Metaverse
Jiawen Kang Xiaofeng Luo Jiangtian Nie Tianhao Wu Haibo Zhou Yonghua Wang Dusit Niyato Shiwen Mao Shengli Xie
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.