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.

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