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Boosting SNARKs and Rate-1 Barrier in Arguments of Knowledge

Authors Jiaqi Cheng, Rishab Goyal

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  • Theory of computation → Computational complexity and cryptography
Keywords
  • SNARGs
  • RAM Delegation

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Abstract

We design a generic compiler to boost any non-trivial succinct non-interactive argument of knowledge (SNARK) to full succinctness. Our results come in two flavors:  
1) For any constant ε > 0, any SNARK with proof size |π| < |ω|/(λ^ε) + poly(λ, |x|) can be upgraded to a fully succinct SNARK, where all system parameters (such as proof/CRS sizes and setup/verifier run-times) grow as fixed polynomials in λ, independent of witness size. 
2) Under an additional assumption that the underlying SNARK has as an efficient knowledge extractor, we further improve our result to upgrade any non-trivial SNARK. For example, we show how to design fully succinct SNARKs from SNARKs with proofs of length |ω| - Ω(λ), or |ω|/(1+ε) + poly(λ, |x|), any constant ε > 0.  Our result reduces the long-standing challenge of designing fully succinct SNARKs to designing arguments of knowledge that beat the trivial construction. It also establishes optimality of rate-1 arguments of knowledge (such as NIZKs [Gentry-Groth-Ishai-Peikert-Sahai-Smith; JoC'15] and BARGs [Devadas-Goyal-Kalai-Vaikuntanathan, Paneth-Pass; FOCS'22]), and suggests any further improvement is tantamount to designing fully succinct SNARKs, thus requires bypassing established black-box barriers [Gentry-Wichs; STOC'11].

Cite As Get BibTex

Jiaqi Cheng and Rishab Goyal. Boosting SNARKs and Rate-1 Barrier in Arguments of Knowledge. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 56:1-56:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICALP.2025.56

Author Details

Jiaqi Cheng
  • University of Wisconsin - Madison, WI, USA
Rishab Goyal
  • University of Wisconsin - Madison, WI, USA

Funding

  • Goyal, Rishab: Support for this research was provided by OVCRGE at UW-Madison with funding from the Wisconsin Alumni Research Foundation.

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