Privacy-Preserving Multimodal 6G-Vehicular Radio Access Scheme via Diffusion Probabilistic Models and Cross-Attention Mechanisms

Multimodal 6G-Vehicular radio access, integrating diverse data modalities such as video, sensor, and positional information with heterogeneous wireless communication technologies, has emerged as a pivotal technology enabling ultrahigh reliability, ultra-low latency, and pervasive connectivity in future vehicular communication scenarios. However, this advanced multimodal integration significantly amplifies privacy risks due to the increased complexity and heterogeneity in mobile vehicular environments. Traditional vehicular communication frameworks often struggle to effectively handle these multidimensional datasets while simultaneously safeguarding sensitive user information against inference attacks. Furthermore, conventional privacy-preserving methods typically introduce considerable computational overhead, which are critical for safetysensitive vehicular applications. To address these challenges, we propose a privacy-preserving multimodal vehicular radio access framework for 6G, integrating Diffusion Probabilistic Models (DPMs) and Cross-Attention Mechanisms (CAMs). Specifically, DPMs safeguard data privacy through generative modeling and differential privacy techniques, while CAMs efficiently fuse multimodal sensor data, reducing latency and enhancing communication reliability. The synergistic integration of DPMs and CAMs provides a dual advantage-robust privacy preservation and efficient multimodal vehicular radio access. The proposed synergy ensures robust privacy, adaptive multimodal integration, and lays a solid foundation for secure and scalable 6G vehicular networks.