End-to-End Differential Privacy in Training Deep Neural Network Classifiers
Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from the parameters of the resulting model. However, existing work often privatizes both training inputs and their labels, and these protections may be conservative when labels are public or can be safely made public. Therefore, in this work we propose a novel private training framework that instead privatizes training inputs while keeping labels public. We consider neural networks with softmax output layers, and thus the mapping from training inputs to the output of the softmax layer is a mapping onto the unit simplex. We randomize softmax out...
| Authors | Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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