Polynomial Threshold Functions for Decision Lists

For $S \subseteq \{0,1\}^n$ a Boolean function $f \colon S \to \{-1,1\}$ is a polynomial threshold function (PTF) of degree $d$ and weight $W$ if there is an integer polynomial $p$ of degree $d$ and with sum of absolute coefficients $W$ such that $f(x) = \text{sign } p(x)$ for all $x \in S$. We study representation of decision lists as PTFs over Boolean cube $\{0,1\}^n$ and over Hamming ball $\{0,1\}^{n}_{\leq k}$. As our first result we show that for all $d = O\left( \left( \frac{n}{\log n}\right)^{1/3}\right)$ any decision list over $\{0,1\}^n$ can be represented by a PTF of degree $d$ and weight $2^{O(n/d^2)}$. This improves the result by Klivans and Servedio by a $\log^2 d$ factor in the exponent of the weight. Our bound is tight for all $d = O\left( \left( \frac{n}{\log n}\right)^{1/3}\right)$ due to the matching lower bound by Beigel. For decision lists over a Hamming ball $\{0,1\}^n_{\leq k}$ we show that the upper bound on the weight above can be drastically improved to $n^{O(\sqrt{k})}$ for $d = \Theta(\sqrt{k})$. We also show that similar improvement is not possible for smaller degree by proving the lower bound $W = 2^{\Omega(n/d^2)}$ for all $d = O(\sqrt{k})$.Comment: 14 page

Sharp Indistinguishability Bounds from Non-Uniform Approximations

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 = ?(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

Near-Optimal Secret Sharing and Error Correcting Codes in AC0

We study the question of minimizing the computational complexity of (robust) secret sharing schemes and error correcting codes. In standard instances of these objects, both encoding and decoding involve linear algebra, and thus cannot be implemented in the class AC0. The feasibility of non-trivial secret sharing schemes in AC0 was recently shown by Bogdanov et al. (Crypto 2016) and that of (locally) decoding errors in AC0 by Goldwasser et al. (STOC 2007). In this paper, we show that by allowing some slight relaxation such as a small error probability, we can construct much better secret sharing schemes and error correcting codes in the class AC0. In some cases, our parameters are close to optimal and would be impossible to achieve without the relaxation. Our results significantly improve previous constructions in various parameters. Our constructions combine several ingredients in pseudorandomness and combinatorics in an innovative way. Specifically, we develop a general technique to simultaneously amplify security threshold and reduce alphabet size, using a two-level concatenation of protocols together with a random permutation. We demonstrate the broader usefulness of this technique by applying it in the context of a variant of secure broadcast