标题： ZKPROV：一种用于大型语言模型数据集来源的零知识方法 显示英文标题

标题： ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models

摘要： 随着大型语言模型（LLMs）在敏感领域的部署不断增加，确保其计算来源的完整性成为一项关键挑战，特别是在医疗保健等受监管的行业，对数据集使用有严格的要求。我们引入了ZKPROV，一种新的密码学框架，能够对LLM来源进行零知识证明。它允许用户验证模型是在可靠数据集上训练的，而不会泄露有关数据集或其参数的敏感信息。与之前专注于完整验证训练过程的方法（导致显著的计算成本）或依赖可信执行环境的方法不同，ZKPROV提供了一种不同的平衡。我们的方法通过零知识证明将训练好的模型与授权训练数据集绑定，同时避免证明每个训练步骤。通过利用数据集签名的元数据和紧凑的模型参数承诺，ZKPROV提供了合理且保护隐私的保证，即LLM的结果来源于在声称的授权和相关数据集上训练的模型。实验结果展示了ZKPROV在生成和验证此证明方面的效率和可扩展性，实现了现实世界部署的实用解决方案。我们还提供了形式化的安全保证，证明我们的方法在确保可信数据集来源的同时，保持了数据集的机密性。 显示英文摘要

摘要： As the deployment of large language models (LLMs) grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a novel cryptographic framework that enables zero-knowledge proofs of LLM provenance. It allows users to verify that a model is trained on a reliable dataset without revealing sensitive information about it or its parameters. Unlike prior approaches that focus on complete verification of the training process (incurring significant computational cost) or depend on trusted execution environments, ZKPROV offers a distinct balance. Our method cryptographically binds a trained model to its authorized training dataset(s) through zero-knowledge proofs while avoiding proof of every training step. By leveraging dataset-signed metadata and compact model parameter commitments, ZKPROV provides sound and privacy-preserving assurances that the result of the LLM is derived from a model trained on the claimed authorized and relevant dataset. Experimental results demonstrate the efficiency and scalability of the ZKPROV in generating this proof and verifying it, achieving a practical solution for real-world deployments. We also provide formal security guarantees, proving that our approach preserves dataset confidentiality while ensuring trustworthy dataset provenance.