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arXiv · 2206.02617

Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent

Da Yu·Gautam Kamath·Janardhan Kulkarni·Tie-Yan Liu·Jian Yin·Huishuai Zhang
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Abstract

Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose output-specific $(\varepsilon,\delta)$-DP to characterize privacy guarantees for individual examples when releasing models trained by DP-SGD. We also design an efficient algorithm to investigate individual privacy across a number of datasets. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. We further discover that the training loss and the privacy parameter of an example are well-correlated. This implies groups that are underserved in terms of model utility simultaneously experience weaker privacy guarantees. For example, on CIFAR-10, the average $\varepsilon$ of the class with the lowest test accuracy is 44.2\% higher than that of the class with the highest accuracy.

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Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang. 2022-06-06. Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent. https://arxiv.org/abs/2206.02617

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