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CRYPTO2024顶会

Mangrove: A Scalable Framework for Folding-Based SNARKs

Wilson D. Nguyen, Trisha Datta, Binyi Chen, Nirvan Tyagi, Dan Boneh

2024年份
13被引次数
6顶会引用

摘要

We present a framework for building efficient folding-based SNARKs. First we develop a new "uniformizing" compiler for NP statements that converts any poly-time computation to a sequence of identical simple steps. The resulting uniform computation is especially well-suited to be processed by a folding-based IVC scheme. Second, we develop two optimizations to folding-based IVC. The first reduces the recursive overhead of the IVC by restructuring the relation to which folding is applied. The second employs a "commit-and-fold'' strategy to further simplify the relation. Together, these optimizations result in a folding based SNARK that has a number of attractive features. First, the scheme uses a constant-size transparent common reference string (CRS). Second, the prover has (i) low memory footprint, (ii) makes only two passes over the data, (iii) is highly parallelizable, and (iv) is concretely efficient. Microbenchmarks indicate that proving time is competitive with leading monolithic SNARKs, and significantly faster than other streaming SNARKs. For 2242^{24} (2322^{32}) gates, the Mangrove prover is estimated to take 22 minutes (88 hours) with peak memory usage approximately 390390 MB (800800 MB) on a laptop.

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