Differentially Private Image Classification by Learning Priors from Random Processes

• URL: http://arxiv.org/abs/2306.06076v2
• Date: Tue, 31 Oct 2023 17:49:45 GMT
• Title: Differentially Private Image Classification by Learning Priors from Random Processes
• Authors: Xinyu Tang, Ashwinee Panda, Vikash Sehwag, Prateek Mittal
• Abstract summary: In privacy-preserving machine learning, differentially private 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.
• Score: 48.0766422536737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\varepsilon \in [1, 8]$. In particular, we improve the previous best reported accuracy on CIFAR10 from $60.6 \%$ to $72.3 \%$ for $\varepsilon=1$.

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