arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2408.00243cs.CRcs.CC
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零知识证明应用的综述

A Survey on the Applications of Zero-Knowledge Proofs

Ryan Lavin, Xuekai Liu, Hardhik Mohanty, Logan Norman, Giovanni Zaarour, Bhaskar Krishnamachari

AI总结:

本文综述了零知识证明在区块链、隐私保护等领域的应用,重点介绍zkSNARKs及其与其他技术的比较,分析了应用领域、性能指标及未来发展方向。

摘要:

零知识证明(ZKPs)通过允许一方在不暴露底层数据的情况下证明陈述的真实性,实现了计算完整性和隐私保护。与同态加密和安全多方计算等替代方案相比,ZKPs在通用性和最小信任假设方面具有独特优势,应用范围涵盖区块链系统和计算任务的保密验证。本文提供了一个技术概述,重点介绍日益相关的子集zkSNARKs。与以往强调算法和理论方面的综述不同,本文从更广泛的视角探讨了实际部署和多个领域的最新应用案例,包括区块链隐私、扩展性、存储和互操作性,以及非区块链应用如投票、认证、时间锁和机器学习。为了支持一致的比较,我们提供了(i)应用领域的分类法,(ii)评估标准,包括证明大小、证明者和验证者时间、内存和设置假设,以及(iii)总结关键权衡和代表系统比较的比较表格。本文还涵盖了支持基础设施,包括零知识虚拟机、领域特定语言、库和框架。尽管强调zkSNARKs因其在部署系统中的普及性,我们将其与zkSTARKs和Bulletproofs进行比较,以澄清透明性和性能权衡。最后,本文总结了未来的研究和应用方向。

英文摘要:

Zero-knowledge proofs (ZKPs) enable computational integrity and privacy by allowing one party to prove the truth of a statement without revealing underlying data. Compared with alternatives such as homomorphic encryption and secure multiparty computation, ZKPs offer distinct advantages in universality and minimal trust assumptions, with applications spanning blockchain systems and confidential verification of computational tasks. This survey provides a technical overview of ZKPs with a focus on an increasingly relevant subset called zkSNARKs. Unlike prior surveys emphasizing algorithmic and theoretical aspects, we take a broader view of practical deployments and recent use cases across multiple domains including blockchain privacy, scaling, storage, and interoperability, as well as non-blockchain applications such as voting, authentication, timelocks, and machine learning. To support consistent comparison, we provide (i) a taxonomy of application areas, (ii) evaluation criteria including proof size, prover and verifier time, memory, and setup assumptions, and (iii) comparative tables summarizing key tradeoffs and representative systems. The survey also covers supporting infrastructure, including zero-knowledge virtual machines, domain-specific languages, libraries, and frameworks. While emphasizing zkSNARKs for their prevalence in deployed systems, we compare them with zkSTARKs and Bulletproofs to clarify transparency and performance tradeoffs. We conclude with future research and application directions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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