Hermes Seal: Zero-Knowledge Assurance for Autonomous Vehicle Communications

arXiv:2603.26343 · cs.CR · Submitted 2026-03-27 · Read on arXiv

cs.CR

Submitted: 2026-03-27

Updated: 2026-03-27

Comments: 28 pages, 7 figures, 4 tables

Journal ref: IEEE Security & Privacy, vol. 24, no. 6, 2026

DOI: 10.1109/MSEC.2026.3735626

Code: https://github.com/mhasan08/zk-AV

Project page: https://www.tesla.com

License: http://creativecommons.org/licenses/by/4.0/

The gist: The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy

Terminology

Abstract

The application of zero-knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk-SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that a statement about its own internal state is true, without revealing sensitive or proprietary data about that state. This paper proposes Hermes Seal: a zk-SNARK-based ZKP framework for enabling privacy-preserving, verifiable communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) networks. The framework allows autonomous systems to generate cryptographic proofs of perception and decision-related computations without revealing proprietary models, sensor data, or internal system states, thereby supporting interoperability across heterogeneous autonomous systems. We present two real-world case studies implemented and empirically evaluated within our framework, demonstrating a step toward verifiable autonomous system information exchanges. The first demonstrates real-time proof generation and verification, achieving 8 ms proof generation and 1 ms verification on a GPU, while the second evaluates the performance of an autonomous vehicle perception stack, enabling proof of computation without exposing proprietary or confidential data. Furthermore, the framework can be integrated into AV perception stacks to facilitate verifiable interoperability and privacy-preserving cooperative perception. The demonstration code for this project is open source, available on Github.

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