Linear Hashing with $\ell_\infty$ guarantees and two-sided Kakeya bounds

TL;DR

This paper demonstrates that random linear maps over finite fields serve as effective hash functions in the ll_ sense, matching the performance of truly random functions up to a constant factor, by connecting hashing with Kakeya bounds.

Contribution

It introduces a novel connection between linear hashing and the finite field Kakeya problem, extending polynomial methods to analyze ll_ guarantees.

Findings

Linear hash functions achieve ll_ guarantees similar to random functions.

Results are tight and match load balancing bounds.

Extends polynomial method techniques to hashing analysis.

Abstract

We show that a randomly chosen linear map over a finite field gives a good hash function in the $\ell_\infty$ sense. More concretely, consider a set $S \subset \mathbb{F}_q^n$ and a randomly chosen linear map $L : \mathbb{F}_q^n \to \mathbb{F}_q^t$ with $q^t$ taken to be sufficiently smaller than $ |S|$. Let $U_S$ denote a random variable distributed uniformly on $S$. Our main theorem shows that, with high probability over the choice of $L$, the random variable $L(U_S)$ is close to uniform in the $\ell_\infty$ norm. In other words, {\em every} element in the range $\mathbb{F}_q^t$ has about the same number of elements in $S$ mapped to it. This complements the widely-used Leftover Hash Lemma (LHL) which proves the analog statement under the statistical, or $\ell_1$, distance (for a richer class of functions) as well as prior work on the expected largest 'bucket size' in linear hash functions [ADMPT99]. By known bounds from the load balancing literature [RS98], our results are tight and show that linear functions hash as well as trully random function up to a constant factor in the entropy loss. Our proof leverages a connection between linear hashing and the finite field Kakeya problem and extends some of the tools developed in this area, in particular the polynomial method.