  Normalizing flow models have risen as a popular solution to the problem of
density estimation, enabling high-quality synthetic data generation as well as
exact probability density evaluation. However, in contexts where individuals
are directly associated with the training data, releasing such a model raises
privacy concerns. In this work, we propose the use of normalizing flow models
that provide explicit differential privacy guarantees as a novel approach to
the problem of privacy-preserving density estimation. We evaluate the efficacy
of our approach empirically using benchmark datasets, and we demonstrate that
our method substantially outperforms previous state-of-the-art approaches. We
additionally show how our algorithm can be applied to the task of
differentially private anomaly detection.
