Oded Goldreich, Amit Sahai, Salil Vadhan

We extend the study of non-interactive statistical zero-knowledge

proofs. Our main focus is to compare the class NISZK of problems

possessing such non-interactive proofs to the class SZK of problems

possessing interactive statistical zero-knowledge proofs. Along these

lines, we first show that if statistical zero knowledge is non-trivial

then so ...
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Salil Vadhan, Amit Sahai

We present the first complete problem for SZK, the class of (promise)

problems possessing statistical zero-knowledge proofs (against an

honest verifier). The problem, called STATISTICAL DIFFERENCE, is to

decide whether two efficiently samplable distributions are either

statistically close or far apart. This gives a new characterization

of SZK that makes ...
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Adam Bouland, Lijie Chen, Dhiraj Holden, Justin Thaler, Prashant Nalini Vasudevan

In both query and communication complexity, we give separations between the class NISZK, containing those problems with non-interactive statistical zero knowledge proof systems, and the class UPP, containing those problems with randomized algorithms with unbounded error. These results significantly improve on earlier query separations of Vereschagin [Ver95] and Aaronson [Aar12] ... more >>>

Inbar Kaslasi, Ron Rothblum, Prashant Nalini Vasudevan

Suppose that a problem $\Pi$ has a statistical zero-knowledge (SZK) proof with communication complexity $m$. The question of batch verification for SZK asks whether one can prove that $k$ instances $x_1,\ldots,x_k$ all belong to $\Pi$ with a statistical zero-knowledge proof whose communication complexity is better than $k \cdot m$ (which ... more >>>