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arXiv · 2107.14582

NeuralDP Differentially private neural networks by design

Moritz Knolle·Dmitrii Usynin·Alexander Ziller·Marcus R. Makowski·Daniel Rueckert·Georgios Kaissis
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Abstract

The application of differential privacy to the training of deep neural networks holds the promise of allowing large-scale (decentralized) use of sensitive data while providing rigorous privacy guarantees to the individual. The predominant approach to differentially private training of neural networks is DP-SGD, which relies on norm-based gradient clipping as a method for bounding sensitivity, followed by the addition of appropriately calibrated Gaussian noise. In this work we propose NeuralDP, a technique for privatising activations of some layer within a neural network, which by the post-processing properties of differential privacy yields a differentially private network. We experimentally demonstrate on two datasets (MNIST and Pediatric Pneumonia Dataset (PPD)) that our method offers substantially improved privacy-utility trade-offs compared to DP-SGD.

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Moritz Knolle, Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis. 2021-07-30. NeuralDP Differentially private neural networks by design. https://arxiv.org/abs/2107.14582

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