一句话：提出 ScaleCirc 框架，通过电路去重、约束性传播和通用化设计，实现对大规模 Circom 电路的高效分析。
零知识证明电路常以 Circom 编写，其中的约束可能无法准确刻画预期计算，导致约束相关缺陷。现有分析工具难以处理包含复杂模板嵌入的大规模电路。ScaleCirc 通过三项技术解决该问题：系统化地利用电路去重策略管理分析冗余；利用源代码语义信息进行约束性传播；提供可泛化到不同电路分析任务的框架。作者在 691 个真实电路上评估，表明其在效率上优于现有工作，并能成功分析许多此前工具无法处理的 Circom 程序。
Zero-knowledge proof (ZKP) circuits implemented in programming languages like Circom are fundamental to blockchain and privacy-preserving applications. These code often suffer from constraint-related issues where constraints fail to accurately specify intended computations. While existing analysis tools have been proposed, they struggle with large-scale circuits containing complex template embeddings. We present ScaleCirc, a novel framework that addresses such limitations through: 1) systematic management of analysis redundancy via circuit deduplication strategies; 2) constrainedness propagation methods leveraging source code semantic information; and 3) a generalizable framework for different circuit analysis tasks. Evaluation on 691 real-world circuits shows ScaleCirc demonstrates higher efficiency, and successfully analyzes many Circom programs that existing works failed on.
DOI 原文 ·
@inproceedings{paperbot9489,
title = {ScaleCirc: Scaling the Analysis over Circom Circuits},
author = {Jinan Jiang and Haoran Qin and Xiapu Luo},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00124}
}