  Generating differentially private (DP) synthetic data that closely resembles
the original private data is a scalable way to mitigate privacy concerns in the
current data-driven world. In contrast to current practices that train
customized models for this task, we aim to generate DP Synthetic Data via APIs
(DPSDA), where we treat foundation models as blackboxes and only utilize their
inference APIs. Such API-based, training-free approaches are easier to deploy
as exemplified by the recent surge in the number of API-based apps. These
approaches can also leverage the power of large foundation models which are
only accessible via their inference APIs. However, this comes with greater
challenges due to strictly more restrictive model access and the need to
protect privacy from the API provider.
  In this paper, we present a new framework called Private Evolution (PE) to
solve this problem and show its initial promise on synthetic images.
Surprisingly, PE can match or even outperform state-of-the-art (SOTA) methods
without any model training. For example, on CIFAR10 (with ImageNet as the
public data), we achieve FID <= 7.9 with privacy cost {\epsilon} = 0.67,
significantly improving the previous SOTA from {\epsilon} = 32. We further
demonstrate the promise of applying PE on large foundation models such as
Stable Diffusion to tackle challenging private datasets with a small number of
high-resolution images. The code and data are released at
https://github.com/microsoft/DPSDA.
