Deploying ZKP Frameworks with Real-World Data: Challenges and Proposed Solutions
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 Fo...
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Main Author | |
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Format | Journal Article |
Language | English |
Published |
12.07.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2307.06408 |