SNARGs for Monotone Policy Batch NP

We construct a succinct non-interactive argument (SNARG ) for the class of monotone policy batch NP languages, under the Learning with Errors (LWE ) assumption. This class is a subclass of NP that is associated with a monotone function f: { 0, 1 }^k→ { 0, 1 } and an NP language L, and contains instances (x₁, …, x_k) such that f(b₁, …, b_k) = 1 where b_j= 1 if and only if x_j∈ L. Our 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 SNARG under standard hardness assumptions for a sub-class of NP that is not known to have a (computational) non-signaling PCP with parameters compatible with the standard framework for constructing SNARG s dating back to [Kalai-Raz-Rothblum, STOC ’13]. Indeed, our approach necessarily departs from this framework. Our construction combines existing quasi-arguments for NP (based on batch arguments for NP ) with a new type of cryptographic encoding of the instance and a new analysis going from local to global soundness. The main novel ingredient used in our encoding is a predicate-extractable hash (PEHash ) family, which is a primitive that generalizes the notion of a somewhere extractable hash. Whereas a somewhere extractable hash allows to extract a single input coordinate, our PEHash extracts a global property of the input. We view this primitive to be of independent interest, and believe that it will find other applications.