Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT

Antti Koskela, Joonas Jälkö, Lukas Prediger +1

We propose a numerical accountant for evaluating the tight $(\epsilon ,\delta )$-privacy loss for algorithms with discrete one dimensional output. The method is based on the privacy lo…