Fourier Sparsity of Delta Functions and Matching Vector PIRs

TL;DR

This paper investigates the Fourier sparsity of delta functions related to Boolean functions, revealing limitations on improving Matching Vector PIR schemes by finding low-sparsity S-decoding polynomials.

Contribution

It establishes nontrivial bounds on the Fourier sparsity of delta functions, demonstrating inherent limitations for enhancing PIR schemes using known matching vector families.

Findings

Bounds on Fourier sparsity of delta functions

Limitations on improving Matching Vector PIRs

No polylogarithmic communication schemes with current methods

Abstract

In this paper we study a basic and natural question about Fourier analysis of Boolean functions, which has applications to the study of Matching Vector based Private Information Retrieval (PIR) schemes. For integers m and r, define a delta function on {0,1}^r to be a function f: Z_m^r -> C with f(0) = 1 and f(x) = 0 for all nonzero Boolean x. The basic question we study is how small the Fourier sparsity of a delta function can be; namely how sparse such an f can be in the Fourier basis? In addition to being intrinsically interesting and natural, such questions arise naturally when studying "S-decoding polynomials" for the known matching vector families. Finding S-decoding polynomials of reduced sparsity, which corresponds to finding delta functions with low Fourier sparsity, would improve the current best PIR schemes. We show nontrivial upper and lower bounds on the Fourier sparsity of delta functions. Our proofs are elementary and clean. These results imply limitations on improving Matching Vector PIR schemes simply by finding better S-decoding polynomials. In particular, there are no S-decoding polynomials that can make Matching Vector PIRs based on the known matching vector families achieve polylogarithmic communication with a constant number of servers. Many interesting questions remain open.