Fast Simulation Algorithms for OLH using Binomial Modeling

Abstract

Optimized Local Hashing (OLH) is a widely used hash-based Local Differential Privacy (LDP) protocol, and simulation-based experimentation is the standard approach for evaluating OLH and OLH-based applications in research. However, the existing OLH simulations have $O(nd)$ computational complexity, where $n$ is the user population size and $d$ is the domain size, and can lead to significant execution times as $n$ and $d$ grow. In this paper, we propose two fast simulation algorithms for OLH (2-Binom and 3-Binom) grounded in Binomial modeling. Our key insight is that, for any domain value $v$, the total number of users whose perturbed reports support $v$ can be decomposed into a sum of two or three Binomial random variables. Using this insight, our algorithms reduce the simulation complexity to $O(n+d)$ without hurting statistical equivalence. In particular, we theoretically prove that both algorithms yield unbiased frequency estimations with variances identical to those of the original OLH simulations. Experiments on real-world datasets confirm that both approaches reduce execution times from several minutes to milliseconds, yielding significant speedups with no change in utility.