Privacy-Preserving Proof of Human Authorship via Zero-Knowledge Process Attestation

TL;DR

This paper introduces ZK-PoP, a zero-knowledge proof system that verifies human authorship through behavioral biometric data without revealing sensitive information, ensuring privacy and authenticity.

Contribution

The paper presents ZK-PoP, a novel zero-knowledge proof construction that enables privacy-preserving process attestation of human authorship using behavioral biometrics.

Findings

Proofs generated in under 30 seconds for a 1-hour session.

192-byte proofs verified in 8.2 milliseconds.

Achieves less than 5% accuracy loss at practical privacy levels.

Abstract

Process attestation verifies human authorship by collecting behavioral biometric evidence, including keystroke dynamics, typing patterns, and editing behavior, during the creative process. However, the very data needed to prove authenticity can reveal intimate details about an author's cognitive state, health conditions, and identity, constituting sensitive biometric data under GDPR Article 9. We resolve this privacy-attestation paradox using zero-knowledge proofs. We present ZK-PoP, a construction that allows a verifier to confirm that (a) sequential work function chains were computed correctly, (b) behavioral feature vectors fall within human population distributions, and (c) content evolution is consistent with incremental human editing, all without learning the underlying behavioral data, exact timing, or intermediate content. Our construction uses Groth16 proofs over arithmetic circuits with Pedersen commitments and Bulletproof range proofs. We prove that ZK-PoP is computationally zero-knowledge, computationally sound, and achieves unlinkability across sessions. Evaluation shows proof generation in under 30 seconds for a 1-hour writing session, with 192-byte proofs verifiable in 8.2 ms, while incurring less than 5% accuracy loss in simulation at practical privacy levels (epsilon >= 1.0) compared to non-private baselines.