|Seminar on Theoretical Machine Learning|
|Topic:||Two approaches to (Deep) Learning with Differential Privacy|
|Date:||Thursday, February 1|
|Time/Room:||12:15pm - 1:45pm/White-Levy Room|
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 crowd-sourced and contain sensitive information. The models should not expose private information in these datasets. Differential Privacy is a standard privacy definition that implies a strong and concrete guarantee on protecting such information.
In this talk, I'll then outline two recent approaches to training deep neural networks while providing a differential privacy guarantee, and some new analysis tools we developed in the process. 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.
Based on joint works with Martin Abadi, Andy Chu, Úlfar Erlingsson, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Daniel Ramage and Li Zhang.