Sharp Indistinguishability Bounds from Non-Uniform Approximations

Author Christopher Williamson

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Christopher Williamson
  • The SW7 Group, Hong Kong, China

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Christopher Williamson. Sharp Indistinguishability Bounds from Non-Uniform Approximations. In 39th International Symposium on Theoretical Aspects of Computer Science (STACS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 219, pp. 59:1-59:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


We study the basic problem of distinguishing between two symmetric probability distributions over n bits by observing k bits of a sample, subject to the constraint that all (k-1)-wise marginal distributions of the two distributions are identical to each other. Previous works of Bogdanov et al. [Bogdanov et al., 2019] and of Huang and Viola [Huang and Viola, 2019] have established approximately tight results on the maximal possible statistical distance between the k-wise marginals of such distributions when k is at most a small constant fraction of n. Naor and Shamir [Naor and Shamir, 1994] gave a tight bound for all k in the special case k = n and when distinguishing with the OR function; they also derived a non-tight result for general k and n. Krause and Simon [Krause and Simon, 2000] gave improved upper and lower bounds for general k and n when distinguishing with the OR function, but these bounds are exponentially far apart when k = Ω(n). In this work we provide sharp upper and lower bounds on the maximal statistical distance that hold for all k and n. Upper bounds on the statistical distance have typically been obtained by providing uniform low-degree polynomial approximations to certain higher-degree polynomials. This is the first work to construct suitable non-uniform approximations for this purpose; the sharpness and wider applicability of our result stems from this non-uniformity.

Subject Classification

ACM Subject Classification
  • Theory of computation → Pseudorandomness and derandomization
  • bounded indistinguishability
  • randomness
  • secret sharing
  • polynomial approximation


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