Optimal Differentially Private Sampling of Unbounded Gaussians

摘要

We provide the first 𝒪(d)-sample algorithm for sampling from unbounded Gaussian distributions under the constraint of (ε, δ)-differential privacy. This is a quadratic improvement over previous results for the same problem, settling an open question of Ghazi, Hu, Kumar, and Manurangsi.