SNARGs for Monotone Policy Batch NP

ePrint Report: SNARGs for Monotone Policy Batch NP

Zvika Brakerski, Maya Farber Brodsky, Yael Tauman Kalai, Alex Lombardi, Omer Paneth

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 …

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