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.

翻译：过程证明通过采集创作过程中的行为生物特征（包括击键动力学、输入模式及编辑行为）来验证人类创作身份。然而，这些用于证明真实性的数据可能泄露作者认知状态、健康状况及身份等私密信息，构成欧盟《通用数据保护条例》第9条定义的敏感生物数据。我们采用零知识证明来解决这一隐私-认证悖论，提出ZK-PoP框架：验证者可在不获知底层行为数据、精确时间戳及中间内容的前提下，确认以下三方面——（a）连续工作函数链被正确计算，（b）行为特征向量符合人类群体分布，（c）内容演变符合渐进式人工编辑特征。本架构采用基于算术电路的Groth16证明，结合Pedersen承诺与Bulletproof范围证明。我们证明ZK-PoP满足计算零知识性、计算可靠性及跨会话不可链接性。评估显示：对于1小时写作场景，证明生成时间小于30秒，192字节证明可在8.2毫秒内完成验证，在实用隐私水平（ε≥1.0）下与无隐私保护基准相比，模拟精度损失低于5%。