Shorter Quasi-Adaptive NIZK Proofs for Linear Subspaces

Abstract

We define a novel notion of quasi-adaptive non-interactive zero knowledge (NIZK) proofs for probability distributions on parametrized languages. It is quasi-adaptive in the sense that the common reference string (CRS) generator can generate the CRS depending on the parameters defining the language. However, the simulation is required to be uniform, i.e., a single efficient simulator should work for the whole class of parametrized languages. For distributions on languages that are linear subspaces of vector spaces over bilinear groups, we give quasi-adaptive NIZKs that are shorter and more efficient than Groth-Sahai NIZKs. For many cryptographic applications quasi-adaptiveNIZKs suffice, and our constructionscan lead to significant improvements in the standard model. Our construction can be based on any k-linear assumption, and in particular under the Symmetric eXternal Diffie Hellman (SXDH) assumption our proofs are even competitive with Random-Oracle based Σ-protocol NIZK proofs. We also show that our system can be extended to include integer tags in the defining equations, where the tags are provided adaptively by the adversary. This leads to applicability of our system to many applications that use tags, e.g. applications using Cramer-Shoup projective