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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Daniele Micciancio, Shien Jin Ong, Amit Sahai, Salil Vadhan

We provide <i>unconditional</i> constructions of <i>concurrent</i>

statistical zero-knowledge proofs for a variety of non-trivial

problems (not known to have probabilistic polynomial-time

algorithms). The problems include Graph Isomorphism, Graph

Nonisomorphism, Quadratic Residuosity, Quadratic Nonresiduosity, a

restricted version of Statistical Difference, and approximate

versions of the (<b>coNP</b> forms of the) Shortest Vector ...
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Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models. This reduction leads to a fully polynomial randomized approximation scheme (FPRAS) for ... more >>>