On the Impossibility of Entropy Reversal, and Its Application to Zero-Knowledge Proofs

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Abstract

Zero knowledge proof systems have been widely studied in cryptography. In the statistical setting, two classes of proof systems studied are Statistical Zero Knowledge (SZK) and Non-Interactive Statistical Zero Knowledge (NISZK), where the difference is that in NISZK only very limited communication is allowed between the verifier and the prover. It is an open problem whether these two classes are in fact equal. In this paper, we rule out efficient black box reductions between SZK and NISZK. We achieve this by studying algorithms which can reverse the entropy of a function. The problem of estimating the entropy of a circuit is complete for NISZK. Hence, reversing the entropy of a function is equivalent to a black box reduction of NISZK to its complement, which is known to be equivalent to a black box reduction of SZK to NISZK [Goldreich et al. CRYPTO 1999]. We show that any such black box algorithm incurs an exponential loss of parameters, and hence cannot be implemented efficiently.

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Lovett, S., & Zhang, J. (2017). On the Impossibility of Entropy Reversal, and Its Application to Zero-Knowledge Proofs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10677 LNCS, pp. 31–55). Springer Verlag. https://doi.org/10.1007/978-3-319-70500-2_2

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