In privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) performs worse than SGD due to per-sample gradient clipping and noise addition. A recent focus in private learning research is improving the performance of DP-SGD on private data by incorporating priors that are learned on real-world public data. In this work, we explore how we can improve the privacy-utility tradeoff of DP-SGD by learning priors from images generated by random processes and transferring these priors to private data. We propose DP-RandP, a three-phase approach. We attain new state-of-the-art accuracy when training from scratch on CIFAR10, CIFAR100, MedMNIST and ImageNet for a range of privacy budgets ε∈[1,8]. In particular, we improve the previous best reported accuracy on CIFAR10 from 60.6% to 72.3% for ε=1.
@article{arxiv.2306.06076,
title = {Differentially Private Image Classification by Learning Priors from Random Processes},
author = {Xinyu Tang and Ashwinee Panda and Vikash Sehwag and Prateek Mittal},
journal= {arXiv preprint arXiv:2306.06076},
year = {2023}
}