The vehicular metaverse is a novel physical-virtual fusion realm that aims to disrupt the current transportation paradigm. Within this landscape, the coexistence of moving vehicles and their digital counterparts inevitably brings new privacy concerns. Pseudonym exchange, where vehicles exchange temporary identifiers with neighbors to enhance anonymity, offers an affordable solution to protect the location privacy of vehicles. However, existing pseudonym exchange schemes primarily focus on physical vehicles, limiting their effectiveness across physical and virtual spaces in the vehicular metaverse. Furthermore, studies have shown that many vehicles care little about their location privacy, so incentivizing more vehicles to participate in pseudonym exchanges remains a challenge. Motivated by these issues, we propose a physical-virtual dual pseudonym exchange scheme, incorporating an Attribute-Matched Double Dutch Auction (AMDDA) incentive mechanism to facilitate pseudonym exchange transactions. We use a trajectory prediction model to evaluate vehicle attributes, ensuring pseudonym exchange between vehicles with high trajectory similarity to enhance location privacy preservation. Furthermore, we devise a Generative Diffusion Model (GDM)-based approach to derive the optimal pricing strategy in the AMDDA market. Extensive experiments on real-world datasets demonstrate that the proposed scheme significantly improves both the efficiency and degree of location privacy protection.

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.