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Public data has been frequently used to improve the privacy-accuracytrade-off of differentially private machine learning, but prior work largelyassumes that this data come from the same distribution as the private. In thiswork, we look at how to use generic large-scale public data to improve thequality of differentially private image generation in Generative AdversarialNetworks (GANs), and provide an improved method that uses public dataeffectively. Our method works under the assumption that the support of thepublic data distribution contains the support of the private; an example ofthis is when the public data come from a general-purpose internet-scale imagesource, while the private data consist of images of a specific type. Detailedevaluations show that our method achieves SOTA in terms of FID score and othermetrics compared with existing methods that use public data, and can generatehigh-quality, photo-realistic images in a differentially private manner.

Large-Scale Public Data Improves Differentially Private Image Generation Quality