Paper 2023/1050

SNARGs for Monotone Policy Batch NP

Zvika Brakerski, Weizmann Institute of Science
Maya Farber Brodsky, Tel Aviv University
Yael Tauman Kalai, Microsoft Research, Massachusetts Institute of Technology
Alex Lombardi, Simons Institute, University of California, Berkeley
Omer Paneth, Tel Aviv University

We construct a succinct non-interactive argument ($\mathsf{SNARG}$) for the class of monotone policy batch $\mathsf{NP}$ languages under the Learning with Errors ($\mathsf{LWE}$) assumption. This class is a subclass of $\mathsf{NP}$ that is associated with a monotone function~$f:\{0,1\}^k\rightarrow\{0,1\}$ and an $\mathsf{NP}$ language $\mathcal L$, and contains instances $(x_1,\ldots,x_k)$ such that $f(b_1,\ldots,b_k)=1$ where $b_j=1$ if and only if $x_j\in \mathcal L$. Our $\mathsf{SNARG}$s are arguments of knowledge in the non-adaptive setting, and satisfy a new notion of somewhere extractability against adaptive adversaries. This is the first $\mathsf{SNARG}$ under standard hardness assumptions for a sub-class of $\mathsf{NP}$ that is not known to have a (computational) non-signaling $\mathsf{PCP}$ with small locality. Indeed, our approach necessarily departs from the known framework of constructing $\mathsf{SNARG}$s dating back to [Kalai-Raz-Rothblum, STOC '13] Our construction combines existing quasi-arguments for $\mathsf{NP}$ (based on batch arguments for $\mathsf{NP}$) with a novel ingredient which we call a predicate-extractable hash ($\mathsf{PEH}$) family. This notion generalizes the notion of a somewhere extractable hash. Whereas a somewhere extractable hash allows to extract a single input coordinate, our $\mathsf{PEH}$ extracts a global property of the input. We view this primitive to be of independent interest, and believe that it will find other applications.

Available format(s)
Publication info
A major revision of an IACR publication in CRYPTO 2023
succinct non-interactive argumentsdelegation of computation
Contact author(s)
zvika brakerski @ weizmann ac il
mayaf2003 @ gmail com
yael @ microsoft com
alexlombardi @ alum mit edu
omerpa @ tauex tau ac il
2023-07-05: approved
2023-07-05: received
See all versions
Short URL
Creative Commons Attribution-NonCommercial-ShareAlike


      author = {Zvika Brakerski and Maya Farber Brodsky and Yael Tauman Kalai and Alex Lombardi and Omer Paneth},
      title = {SNARGs for Monotone Policy Batch NP},
      howpublished = {Cryptology ePrint Archive, Paper 2023/1050},
      year = {2023},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.