Ou: Automating the Parallelization of Zero-Knowledge Protocols
Yuyang Sang, Ning Luo, Samuel Judson, Ben Chaimberg, Timos Antonopoulos, Xiao Wang, Ruzica Piskac, Zhong Shao
摘要
A zero-knowledge proof (ZKP) is a powerful cryptographic primitive used in many decentralized or privacy-focused applications. However, the high overhead of ZKPs can restrict their practical applicability. We design a programming language, Ou, aimed at easing the programmer's burden when writing efficient ZKPs, and a compiler framework, Lian, that automates the analysis and distribution of statements to a computing cluster. Lian uses programming language semantics, formal methods, and combinatorial optimization to automatically partition an Ou program into efficiently sized chunks for parallel ZK-proving and/or verification. We contribute: (1) A front-end language where users can write proof statements as imperative programs in a familiar syntax; (2) A compiler architecture and implementation that automatically analyzes the program and compiles it into an optimized IR that can be lifted to a variety of ZKP constructions; and (3) A cutting algorithm, based on Pseudo-Boolean optimization and Integer Linear Programming, that reorders instructions and then partitions the program into efficiently sized chunks for parallel evaluation and efficient state reconciliation.
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引用它的顶会 Paper3
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- V-ORAM: A Versatile and Adaptive ORAM Framework with Service Transformation for Dynamic WorkloadsBo Zhang, Helei Cui, Xingliang Yuan, Zhiwen Yu 等USENIX Security 2025
它引用的顶会 Paper14
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- Doubly-Efficient zkSNARKs Without Trusted SetupRiad S. Wahby, Ioanna Tzialla, Abhi Shelat, Justin Thaler 等S&P 2018 · 被引用 356 次
- Spartan: Efficient and General-Purpose zkSNARKs Without Trusted SetupSrinath T. V. SettyCRYPTO 2020 · 被引用 262 次
- ZEXE: Enabling Decentralized Private ComputationSean Bowe, Alessandro Chiesa, Matthew Green, Ian Miers 等S&P 2020 · 被引用 257 次
- Wolverine: Fast, Scalable, and Communication-Efficient Zero-Knowledge Proofs for Boolean and Arithmetic CircuitsChenkai Weng, Kang Yang, Jonathan Katz, Xiao WangS&P 2021 · 被引用 205 次
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