Care-AI Seminar Series: Protecting Individual Privacy in Machine Learning

Modern machine learning systems are trained on massive amounts of data. It turns out that, without special care, machine learning models are prone to regurgitating or otherwise revealing information about individual data points. This is problematic when parts of the training data are sensitive or contain private information, as is commonly the case in many settings of interest. Dr. Kamath will discuss differential privacy, a rigorous notion of data privacy, and how it can be used to provably protect against such inadvertent data disclosures by machine learning models. He will focus particularly on recent approaches that simultaneously employ both non-sensitive and sensitive data, granting significant improvements in the utility of privately trained models. He will also discuss pitfalls with these methods and potential paths forward for the field.

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