PrivacyBuDe: Privacy Buckets Demo Tight Differential Privacy Guarantees made Simple

Computing differential privacy guarantees is an important task for a wide variety of applications. The tighter the guarantees are, the more difficult it seems to be to compute them: naive bounds are simple additions, whereas modern composition theorems require either a search over a potentially confusing parameter space (for the composition theorem of Kairouz, Oh, and Visvanath), require computing many moments (for Rényi DP) or finding parameters of a function that limits the privacy loss (for concentrated DP). The best known approach for tight differential privacy bounds, called Privacy Buckets, provides requires running a fairly complex implementation of numerical approximations. In this work, we provide an easy-to-use interface for computing state-of-the-art differential privacy guarantees by simply accessing a website. Guarantees for the widely used Laplace mechanism and for the similarly popular Gauss mechanism can be computed by simply stating the scale parameter of the noise, the sensitivity, and the number of compositions. Privacy guarantees for more complex distributions can be computed by uploading two histograms. This work bridges the gap between the best known theoretical results for computing differential privacy guarantees and privacy analysts and users benefiting from such guarantees.