  As machine learning has become more relevant for everyday applications, a
natural requirement is the protection of the privacy of the training data. When
the relevant learning questions are unknown in advance, or hyper-parameter
tuning plays a central role, one solution is to release a differentially
private synthetic data set that leads to similar conclusions as the original
training data. In this work, we introduce an algorithm that enjoys fast rates
for the utility loss for sparse Lipschitz queries. Furthermore, we show how to
obtain a certificate for the utility loss for a large class of algorithms.
