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A Zero-Knowledge Signature Framework for Efficient Post-Quantum Message Authentication in Cooperative Automated Driving
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arXiv:2610.11490v1 Announce Type: new Abstract: Connected and Automated Vehicles (CAV) rely on authenticated Vehicle-to-Everything (V2X) communications to exchange safety-critical information among vehicles and roadside infrastructure. As the automotive industry transitions toward post-quantum cryptography (PQC), the significantly larger public keys and signatures of standardized PQC digital signature algorithms introduce substantial communication overhead, which challenges the scalability of certificate-based V2X authentication, particularly for high-frequency cooperative awareness messages (CAM). This paper presents ZKS-PQC, a zero-knowledge signature framework that enables communication-efficient post-quantum message authentication for cooperative V2X systems. Instead of transmitting complete post-quantum public keys and signatures, the proposed framework replaces this authentication material with a compact Zero-Knowledge Proof (ZKP) while preserving compatibility with existing certificate-based trust architectures. This approach supports incremental migration and backward compatibility with the legacy ECDSA. The framework is implemented using the Open Quantum Safe (liboqs) and ZKP (Bulletproofs) libraries, and we evaluated the performance of standardized NIST PQC signature algorithms and additional candidate algorithms. Experimental validation on both a Linux platform and a commercial On-Board Unit (OBU) demonstrates substantial reductions in message size exceeding 95% for all algorithms, while limiting additional processing overhead by staying within the order of milliseconds at both sender and receiver in many algorithms, consistent with the latency requirements of real-time V2X operation. By decoupling communication overhead from the size characteristics of post-quantum signature algorithms, ZKS-PQC offers a practical migration strategy for scalable, quantum-resilient message authentication in future CAV systems.
Source:https://arxiv.org/abs/2610.11490PreviousMARC: Multi-Bit Watermarking for Autoregressive Audio Generation against Codec AttacksNextEIFL: Efficiently Protecting Global Model Privacy and Integrity Against an Untrusted Server in Federated Learning
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