New Distinguishers for Negation-Limited Weak Pseudorandom Functions

TL;DR

This paper introduces new algorithms to distinguish negation-limited circuits from random functions more efficiently than previous methods, using Fourier analysis on slices of the Boolean cube.

Contribution

It presents novel Fourier-analytic techniques on Boolean slices to improve distinguishers and weak learners for negation-limited circuits.

Findings

New distinguisher runs in exp(˜O(n^{1/3}k^{2/3})) time

Previous best required exp(˜O(n^{1/2}k)) time

Improved weak learner for negation-limited circuits

Abstract

We show how to distinguish circuits with $\log k$ negations (a.k.a $k$-monotone functions) from uniformly random functions in $\exp\left(\tilde{O}\left(n^{1/3}k^{2/3}\right)\right)$ time using random samples. The previous best distinguisher, due to the learning algorithm by Blais, Cannone, Oliveira, Servedio, and Tan (RANDOM'15), requires $\exp\big(\tilde{O}(n^{1/2} k)\big)$ time. Our distinguishers are based on Fourier analysis on \emph{slices of the Boolean cube}. We show that some "middle" slices of negation-limited circuits have strong low-degree Fourier concentration and then we apply a variation of the classic Linial, Mansour, and Nisan "Low-Degree algorithm" (JACM'93) on slices. Our techniques also lead to a slightly improved weak learner for negation limited circuits under the uniform distribution.