Machine Learning in Montpellier, Theory & Practice

Machine learning’s success relies on the use of datasets that typically hold sensitive information about people. To prevent leakage of personal data, differentially private optimization methods have recently been developed. While these methods offer strong guarantees on data privacy, they suffer from degraded performance. This incurs a trade-off between data privacy and model utility. In this talk, I will describe the difficulties faced when training a model with differential privacy guarantees, in particular for high-dimensional models. I will then show how coordinate descent methods can exploit structural properties of the problem to improve the privacy-utility trade-off.

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