The complexity of estimating min-entropy
Abstract
Goldreich et al. (CRYPTO 1999) proved that the promise problem for estimating the Shannon entropy of a distribution sampled by a given circuit is NISZK-complete. We consider the analogous problem for estimating the min-entropy and prove that it is SBP-complete, where SBP is the class of promise problems that correspond to approximate counting of NP witnesses. The result holds even when the sampling circuits are restricted to be 3-local. For logarithmic-space samplers, we observe that this problem is NP-complete by a result of Lyngsø and Pedersen on hidden Markov models (JCSS 65(3):545–569, 2002).
Publication Title
Computational Complexity
Recommended Citation
Watson, T. (2016). The complexity of estimating min-entropy. Computational Complexity, 25 (1), 153-175. https://doi.org/10.1007/s00037-014-0091-2
