Deploying ZKP Frameworks with Real-World Data: Challenges and Proposed Solutions

TL;DR

This paper introduces Fact Fortress, a comprehensive framework for deploying zero-knowledge proofs with real-world data, addressing challenges like scalability and interoperability while ensuring data privacy and integrity.

Contribution

The paper presents Fact Fortress, a novel end-to-end ZKP framework that leverages data provenance and auditable policies for secure, private, and scalable data handling outside blockchain.

Findings

Fair scalability demonstrated for large datasets

Framework enables on-chain verification without data exposure

Provides high-level abstractions for complex computations

Abstract

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.