Large-Scale Public Data Improves Differentially Private Image Generation Quality. (arXiv:2309.00008v1 [cs.CV])

Public data has been frequently used to improve the privacy-accuracy
trade-off of differentially private machine learning, but prior work largely
assumes that this data come from the same distribution as the private. In this
work, we look at how to use generic large-scale public data to improve the
quality of differentially private image generation in Generative Adversarial
Networks (GANs), and provide an improved method that uses public data
effectively. Our method works under the assumption that the support of the …

accuracy data distribution image image generation large machine machine learning privacy private public quality scale trade work