This repository contains code for the paper "Privacy by Postprocessing the Discrete Laplace Mechanism" by Quentin Hillebrand, Jacob Imola, Rasmus Pagh, and Sia Sejer. It shows how data made private using discrete Laplace noise can be post-processed to yield a simple, unbiased estimator of any subexponential function f of the original data, giving a simple, discrete, multivariate version of the recent unbiasing result for the Laplace mechanism by Calmon et al. (FORC '25). For more details see https://arxiv.org/abs/2605.06502