  Increasing interest in privacy-preserving machine learning has led to new and
evolved approaches for generating private synthetic data from undisclosed real
data. However, mechanisms of privacy preservation can significantly reduce the
utility of synthetic data, which in turn impacts downstream tasks such as
learning predictive models or inference. We propose several re-weighting
strategies using privatised likelihood ratios that not only mitigate
statistical bias of downstream estimators but also have general applicability
to differentially private generative models. Through large-scale empirical
evaluation, we show that private importance weighting provides simple and
effective privacy-compliant augmentation for general applications of synthetic
data.
