Privacy-Preserving AI Authentication Protocols for Secure and Scalable Vehicular Intelligence Networks

Abstract

The issue dealt with in this article is the increasing difficulty of secure and privacy-resistant authentication in Vehicular Intelligence Networks (VINs) which are becoming more and more based on AI-driven decision making and distributed learning. Current automobile authentication systems, mostly founded on the public key infrastructures, pseudonym certificates, or independent cryptographic primitives, fail to meet high-latency constraints, scalability in congested traffic, and the high levels of identity disclosure, tracking, and AI-based inference resistant solutions. In order to fill this gap, the paper offers a hybrid privacy-saving authentication system, which closely combines lightweight cryptographic tools with AI-assisted trust certifications and privacy-sensitive machine learning approaches. A detailed threat and privacy analysis introduces resistance to impersonation and replay attacks, the Sybil, and AI-specific attacks, retaining anonymity, unlikability, and conditional traceability. Performance analysis through simulation in realistic vehicular environment demonstrates that the protocol can be used to attain low authentication latency, less communication overhead and high authentication accuracy in even high-mobility and high-density environments. All in all, the paper has shown that the implementation of AI authentication that is privacy conscious can be feasibly implemented in VINs without compromising real-time behavior to give intelligent transport systems scalable and future-proof security baseline. These results provide practical advice to researchers, standards organizations, and practitioners developing next-generation V2X security frameworks that need to balance automation, accountability, regulatory provisions, and scale user privacy in the developed and deployed globally and sustainably.