Welcome to the resource topic for 2024/416
Title:
Mangrove: A Scalable Framework for Folding-based SNARKs
Authors: Wilson Nguyen, Trisha Datta, Binyi Chen, Nirvan Tyagi, Dan Boneh
Abstract: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 proving time is comparable to leading monolithic SNARKs, and is significantly faster than other streaming SNARKs. On a laptop, for 2^{24} (2^{32}) gates, the Mangrove prover is estimated to take 2 minutes (8 hours) with peak memory usage approximately 390 MB (800 MB).
ePrint: https://eprint.iacr.org/2024/416
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