The problem of learning from data while preserving the privacy of individual observations has a long history and spans over multiple disciplines [1–3]. One way to preserve privacy is to corrupt the learning procedure with noise without destroying the information that we want to extract. Differential Privacy (DP) is one of the most powerful tools in this context [3, 4].