Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning con-text, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often excessively reduces accuracy. Bayesian differential privacy (BDP) takes into account the data distribution to provide more practical privacy guarantees.