Nirvan Tyagi

Mangrove: A Scalable Framework for Folding-based SNARKs

Wilson Nguyen, Trisha Datta, Binyi Chen, Nirvan Tyagi, Dan Boneh
CRYPTO 2024

Materials

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.