Machine learning techniques based on neural networks are achieving remarkable
results in a wide variety of domains. Often, the training of models requires large,
representative datasets, which may be crowdsourced and contain sensitive
information. The models should not expose private information in these datasets.
Addressing this goal, we develop new algorithmic techniques for learning and a
refined analysis of privacy costs within the framework of differential privacy. Our
implementation and experiments demonstrate that we can train deep neural networks
with non-convex objectives, under a modest privacy budget, and at a manageable cost
in software complexity, training efficiency, and model quality.