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
TL;DR
This work tackles privacy during VT migrations in vehicular edge metaverses by introducing a blockchain-based cross-metaverse dual pseudonym management framework. It combines a hierarchical main/relay/subchain architecture with Local Metaverse Managers to securely issue, distribute, and revoke VMU/VT pseudonyms via cross-chain coordination, guided by a new Degree of Privacy Entropy ($DoPE$) metric and an inventory-theory based social welfare formulation. To handle dynamic pseudonym demands, the authors develop a multi-agent MADRL solution (MAPPO) for optimal pseudonym generation under a POMDP formulation, achieving centralized training and decentralized execution. Empirical results demonstrate improved privacy protection, reduced cross-chain consensus times, and superior learning efficiency, indicating practical viability for real-world vehicular edge metaverses.
Abstract
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.