Appears in collection : Meeting in Mathematical Statistics: Statistical thinking in the age of AI : robustness, fairness and privacy / Rencontre de Statistique Mathématique

I will provide a broad overview of differential privacy, which provides guarantees that a data analysis protects the privacy of data contributors. The main focus will be on the private computation and release of different statistics, both classical (low-dimensional) and high-dimensional statistics. In addition to giving a high-level program for the development of optimal private estimators, I will likely discuss a few open questions as well.