Zero-knowledge proof (ZKP) frameworks have the potential to revolutionize the handling of sensitive data in various domains. However, deploying ZKP frameworks with real-world data presents several challenges, including scalability, usability, and interoperability. In this project, we present Fact Fortress, an end-to-end framework for designing and deploying zero-knowledge proofs of general statements. Our solution leverages proofs of data provenance and auditable data access policies to ensure the trustworthiness of how sensitive data is handled and provide assurance of the computations that have been performed on it. ZKP is mostly associated with blockchain technology, where it enhances transaction privacy and scalability through rollups, addressing the data inherent to the blockchain. Our approach focuses on safeguarding the privacy of data external to the blockchain, with the blockchain serving as publicly auditable infrastructure to verify the validity of ZK proofs and track how data access has been granted without revealing the data itself. Additionally, our framework provides high-level abstractions that enable developers to express complex computations without worrying about the underlying arithmetic circuits and facilitates the deployment of on-chain verifiers. Although our approach demonstrated fair scalability for large datasets, there is still room for improvement, and further work is needed to enhance its scalability. By enabling on-chain verification of computation and data provenance without revealing any information about the data itself, our solution ensures the integrity of the computations on the data while preserving its privacy.

翻译：零知识证明（ZKP）框架有望彻底改变各领域对敏感数据的处理方式。然而，使用真实世界数据部署ZKP框架面临多项挑战，包括可扩展性、可用性和互操作性。在本项目中，我们提出了Fact Fortress，这是一个用于设计和部署通用语句零知识证明的端到端框架。我们的解决方案利用数据溯源证明和可审计的数据访问策略，确保敏感数据处理的可靠性，并为已执行的计算提供保证。ZKP主要与区块链技术相关联，通过rollup增强交易隐私和可扩展性，处理区块链固有数据。我们的方法则侧重于保护区块链外部数据的隐私，将区块链作为公开可审计的基础设施，用于验证ZK证明的有效性并跟踪数据访问的授权过程，同时不泄露数据本身。此外，我们的框架提供高级抽象，使开发者能够表达复杂计算而无需关注底层算术电路，并简化链上验证器的部署。尽管我们的方法在大型数据集上展现出良好的可扩展性，但仍存在改进空间，需要进一步工作以增强其可扩展性。通过在不泄露数据本身任何信息的情况下实现计算和数据溯源的链上验证，我们的解决方案在保护数据隐私的同时确保了数据计算的完整性。