Cross-reality Location Privacy Protection in 6G-enabled Vehicular Metaverses: An LLM-enhanced Hybrid Generative Diffusion Model-based Approach

2026-01-18

AI Summary

This study addresses the cross-reality location privacy leakage risk faced by autonomous vehicles in 6G-enabled vehicular metaverse environments, where adversaries can infer vehicle trajectories by correlating physical locations with the deployment positions of virtual AI agents. To mitigate this threat, the authors propose a hybrid privacy-preserving framework that integrates continuous location perturbation with discrete privacy-aware AI agent migration. They introduce “cross-reality location entropy” as a novel privacy metric and develop an LLM-enhanced Hybrid Diffusion Proximal Policy Optimization (LHDPPO) algorithm, which leverages a large language model-based reward mechanism and dual generative diffusion models for policy exploration. Experimental results on real-world datasets demonstrate that the proposed approach effectively suppresses cross-reality location privacy leakage while maintaining low latency, high service quality, and an immersive user experience.

Technology Category

• Machine Learning: Privacy
• Multiagent Systems: Adversarial Agents
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks. The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge servers to support intelligent driving decision making and enhanced on-board experiences. However, such cross-reality interactions may cause serious location privacy risks, as adversaries can infer AV trajectories by correlating the location reported when AVs request LBS in reality with the location of the edge servers on which their corresponding AI agents are deployed in virtuality. To address this challenge, we design a cross-reality location privacy protection framework based on hybrid actions, including continuous location perturbation in reality and discrete privacy-aware AI agent migration in virtuality. In this framework, a new privacy metric, termed cross-reality location entropy, is proposed to effectively quantify the privacy levels of AVs. Based on this metric, we formulate an optimization problem to optimize the hybrid action, focusing on achieving a balance between location protection, service latency reduction, and quality of service maintenance. To solve the complex mixed-integer problem, we develop a novel LLM-enhanced Hybrid Diffusion Proximal Policy Optimization (LHDPPO) algorithm, which integrates LLM-driven informative reward design to enhance environment understanding with double Generative Diffusion Models-based policy exploration to handle high-dimensional action spaces, thereby enabling reliable determination of optimal hybrid actions. Extensive experiments on real-world datasets demonstrate that the proposed framework effectively mitigates cross-reality location privacy leakage for AVs while maintaining strong user immersion within 6G-enabled vehicular metaverse scenarios.

Problem

• cross-reality
• location privacy
• vehicular metaverse
• 6G
• autonomous vehicles

Innovation

• Cross-reality privacy
• LLM-enhanced reinforcement learning
• Generative diffusion model
• Vehicular metaverse
• Location entropy