Room 02.022, Building 5, St Priest campus Machine Learning in Montpellier, Theory & Practice Personal data is being collected at an unprecedented scale by businesses and public organizations, driven by the progress of data science and machine learning. While such data can be turned into useful knowledge about the global population by computing aggregate statistics or training machine learning models, this can also lead to undesirable disclosure of personal information. We must therefore deal with two conflicting objectives: maximizing the utility of data while protecting the privacy of individuals whose data is used in the analysis. In this talk, I will present differential privacy (DP), a statistical definition of privacy which comes with rigorous guarantees as well as an algorithmic framework that allows the design of practical privacy-preserving algorithms. I will then discuss the application of DP to machine learning, and some related open questions. Machine Learning in Montpellier, Theory & Practice

Room 109, IMAG, Triolet campus 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. Machine Learning in Montpellier, Theory & Practice