Privacy-Preserving Proof of Human Authorship via Zero-Knowledge Process Attestation
For authors and verifiers needing to prove human authorship (e.g., against AI-generated content) while complying with GDPR, ZK-PoP resolves the privacy-attestation paradox.
ZK-PoP enables privacy-preserving proof of human authorship by using zero-knowledge proofs to verify behavioral biometrics and content evolution without revealing sensitive data, achieving under 30-second proof generation for a 1-hour session and 8.2 ms verification with less than 5% accuracy loss at epsilon >= 1.0.
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.