CryptoDB
Orion: Zero Knowledge Proof with Linear Prover Time
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| Conference: | CRYPTO 2022 |
| Abstract: | Zero-knowledge proof is a powerful cryptographic primitive that has found various applications in the real world. However, existing schemes suffer from a high overhead on the proof generation time that is super-linear in the size of the statement represented as an arithmetic circuit, limiting their efficiency and scalability in practice. In this paper, we present Orion, a new zero-knowledge argument system that achieves $O(N)$ prover time of field operations and hash functions and $O(\log^2 N)$ proof size. Orion is concretely efficient and our implementation shows that the prover time is 3.09s and the proof size is 1.5MB for a circuit with $2^{20}$ multiplication gates. The prover time is the fastest among all existing systems, and the proof size is an order of magnitude smaller than a recent scheme in Golovnev et al. 2021. In particular, our scheme proposes two new techniques leading to the efficiency improvement. (1) We propose a new algorithm to test whether a random bipartite graph is a lossless expander graph or not based on the densest subgraph algorithm. It allows us to sample lossless expanders with an overwhelming probability. The technique improves the efficiency and/or security of all existing zero-knowledge argument schemes with a linear prover time. The testing algorithm based on densest subgraph may be of independent interest for other applications of expander graphs. (2) We develop an efficient proof composition scheme, code switching, to reduce the proof size from square root to polylogarithmic. The scheme is built on the encoding circuit of a linear code and shows that the witness of a second zero-knowledge argument is the same as the message in the linear code. The proof composition only introduces a small overhead on the prover time. |
Video from CRYPTO 2022
BibTeX
@inproceedings{crypto-2022-32215,
title={Orion: Zero Knowledge Proof with Linear Prover Time},
publisher={Springer-Verlag},
author={Tiancheng Xie and Yupeng Zhang and Dawn Song},
year=2022
}