Adaptive Differentially Private Empirical Risk Minimization

• URL: http://arxiv.org/abs/2110.07435v1
• Date: Thu, 14 Oct 2021 15:02:20 GMT
• Title: Adaptive Differentially Private Empirical Risk Minimization
• Authors: Xiaoxia Wu and Lingxiao Wang and Irina Cristali and Quanquan Gu and Rebecca Willett
• Abstract summary: We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization. We prove that the ADP method considerably improves the utility guarantee compared to the standard differentially private method in which vanilla random noise is added.
• Score: 95.04948014513226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization. At each iteration, the random noise added to the gradient is optimally adapted to the stepsize; we name this process adaptive differentially private (ADP) learning. Given the same privacy budget, we prove that the ADP method considerably improves the utility guarantee compared to the standard differentially private method in which vanilla random noise is added. Our method is particularly useful for gradient-based algorithms with time-varying learning rates, including variants of AdaGrad (Duchi et al., 2011). We provide extensive numerical experiments to demonstrate the effectiveness of the proposed adaptive differentially private algorithm.

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