Zero-Knowledge Proof Frameworks: A Survey

2025-02-10

AI Summary

Despite the proliferation of open-source zero-knowledge proof (ZKP) frameworks, there is no systematic empirical evaluation, hindering framework selection, reproducibility, and deployment. Method: This paper presents the first unified, horizontal evaluation of 25 mainstream ZKP frameworks—including Circom, Arkworks, and SnarkJS—across usability, SHA-256 and matrix multiplication performance, and deployment complexity. We introduce a standardized Docker-based development environment and a reproducible benchmark suite, enabling fair and consistent comparison. Contribution/Results: We propose a use-case-driven framework selection guide and evolutionary roadmap; deliver quantitative comparisons of throughput, proof generation/verification latency, and memory overhead; and fully open-source all code, configuration tools, and evaluation reports. This work significantly lowers the engineering barrier for ZKP adoption and provides a rigorous, empirically grounded foundation for both academic research and industrial deployment.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Responsible Web: Consent frameworks and practices on the web
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Data transparency and provenance

Abstract

Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret itself. ZKPs have shown to be an extremely powerful tool, as evidenced in both industry and academic settings. In recent years, the utilization of user data in practical applications has necessitated the rapid development of privacy-preserving techniques, including ZKPs. This has led to the creation of several robust open-source ZKP frameworks. However, there remains a significant gap in understanding the capabilities and real-world applications of these frameworks. Furthermore, identifying the most suitable frameworks for the developers' specific applications and settings is a challenge, given the variety of options available. The primary goal of our work is to lower the barrier to entry for understanding and building applications with open-source ZKP frameworks. In this work, we survey and evaluate 25 general-purpose, prominent ZKP frameworks. Recognizing that ZKPs have various constructions and underlying arithmetic schemes, our survey aims to provide a comprehensive overview of the ZKP landscape. These systems are assessed based on their usability and performance in SHA-256 and matrix multiplication experiments. Acknowledging that setting up a functional development environment can be challenging for these frameworks, we offer a fully open-source collection of Docker containers. These containers include a working development environment and are accompanied by documented code from our experiments. We conclude our work with a thorough analysis of the practical applications of ZKPs, recommendations for ZKP settings in different application scenarios, and a discussion on the future development of ZKP frameworks.

Problem

• Survey and evaluate 25 ZKP frameworks.
• Lower barrier for ZKP framework understanding.
• Provide practical ZKP application recommendations.

Innovation

• Survey of 25 ZKP frameworks
• Performance in SHA-256 experiments
• Open-source Docker containers provided