首个实现亚线性内存的零知识证明系统,让手机也能运行隐私计算。
Zero-Knowledge Proofs in Sublinear Space
- 分块处理计算,用树状算法将内存需求从线性降至平方根级
- 计算规模T下内存从Θ(T)降至O(√T + logT·loglogT),证明时间不变
- 兼容主流承诺方案,适合移动端、边缘设备及大规模可信计算
零知识证明允许在不泄露私密信息的情况下验证计算,但现有系统内存需求与计算规模成正比,长期限制了其在大规模应用和移动、边缘设备上的使用。本文首次提出适用于主流密码构造的亚线性内存证明系统。通过分块处理并采用空间高效的树算法,将内存复杂度从Θ(T)降至O(√T + logT·loglogT),同时保持恒定的流水线遍历次数,证明生成时间不变。对于广泛使用的线性多项式承诺方案(如KZG/IPA),本方法在相同参数下生成完全一致的证明与验证结果,仅需对聚合承诺进行哈希以生成挑战,保留原有证明大小与安全性。基于哈希的系统也实现平方根级内存缩减,尽管证明结构略有不同。该进展使零知识证明可在日常设备上运行,让以往无法实现的大规模计算具备可验证性,从根本上推动隐私计算的普及。高效零知识系统为数字系统中的信任建立带来变革——从促进去中心化网络的广泛参与,到实现前所未有的可验证科学计算。
原文摘要 · Abstract (English)
Zero-knowledge proofs allow verification of computations without revealing private information. However, existing systems require memory proportional to the computation size, which has historically limited use in large-scale applications and on mobile and edge devices. We solve this fundamental bottleneck by developing, to our knowledge, the first proof system with sublinear memory requirements for mainstream cryptographic constructions. Our approach processes computations in blocks using a space-efficient tree algorithm, reducing memory from linear scaling to square-root scaling--from $Θ(T)$ to $O(\sqrt{T} + \log T \log\log T)$ for computation size $T$--while maintaining the same proof generation time through a constant number of streaming passes. For widely-used linear polynomial commitment schemes (KZG/IPA), our method produces identical proofs and verification when using the same parameters and hashing only aggregate commitments into the challenge generation, preserving proof size and security. Hash-based systems also achieve square-root memory scaling though with slightly different proof structures. This advance enables zero-knowledge proofs on everyday devices and makes previously infeasible large computations verifiable, fundamentally democratizing access to privacy-preserving computation. Space-efficient zero knowledge proof systems create opportunities to reshape how trust is established in digital systems--from enabling widespread participation in decentralized networks to making verifiable scientific computing practical at unprecedented scales.
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