# Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses

arXiv: https://arxiv.org/abs/2309.00477 · HTML: https://mlsanity.com/paper/2309.00477/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2023.

## Built on by (most cited first, 10 of 10)

- [Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2407.11036/) (2024)
- [Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach](https://mlsanity.com/paper/2401.09680/) (2024)
- [Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse](https://mlsanity.com/paper/2403.15285/) (2024)
- [Digital Twin in Industries: A Comprehensive Survey](https://mlsanity.com/paper/2412.00209/) (2024)
- [On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks](https://mlsanity.com/paper/2403.04430/) (2024)
- [Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses](https://mlsanity.com/paper/2406.05422/) (2024)
- [Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks](https://mlsanity.com/paper/2409.15695/) (2024)
- [Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses](https://mlsanity.com/paper/2505.12710/) (2025)
- [A Multi-Agent DRL-Based Framework for Optimal Resource Allocation and Twin Migration in the Multi-Tier Vehicular Metaverse](https://mlsanity.com/paper/2502.19004/) (2025)
- [Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approach](https://mlsanity.com/paper/2601.12311/) (2026)

## Abstract (arXiv)

The gradual fusion of intelligent transportation systems with metaverse technologies is giving rise to vehicular metaverses, which blend virtual spaces with physical space. As indispensable components for vehicular metaverses, Vehicular Twins (VTs) are digital replicas of Vehicular Metaverse Users (VMUs) and facilitate customized metaverse services to VMUs. VTs are established and maintained in RoadSide Units (RSUs) with sufficient computing and storage resources. Due to the limited communication coverage of RSUs and the high mobility of VMUs, VTs need to be migrated among RSUs to ensure real-time and seamless services for VMUs. However, during VT migrations, physical-virtual synchronization and massive communications among VTs may cause identity and location privacy disclosures of VMUs and VTs. In this article, we study privacy issues and the corresponding defenses for VT migrations in vehicular metaverses. We first present four kinds of specific privacy attacks during VT migrations. Then, we propose a VMU-VT dual pseudonym scheme and a synchronous pseudonym change framework to defend against these attacks. Additionally, we evaluate average privacy entropy for pseudonym changes and optimize the number of pseudonym distribution based on inventory theory. Numerical results show that the average utility of VMUs under our proposed schemes is 33.8% higher than that under the equal distribution scheme, demonstrating the superiority of our schemes.