Giovanni Cherubin

Giovanni Cherubin

Dr. Giovanni Cherubin is a Senior Researcher at Microsoft (Cambridge), working with the Microsoft Security Response Centre (MSRC). Before joining Microsoft, he held research positions at the Alan Turing Institute and EPFL, and he obtained a PhD from Royal Holloway University of London in Machine Learning and Cyber Security. His research focuses on privacy and security properties of machine learning models, and on the theoretical/empirical study of their information leakage. He also works on reliable machine learning tools, such as distribution-free uncertainty estimation for machine learning (e.g., Conformal Prediction).

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A threat-specific look at Privacy-Preserving Machine Learning


The hope to train machine learning models whilst ensuring the privacy of their training data is within reach, but it requires good care. To succeed, one needs to carefully analyse how and where they plan to deploy the model, and decide which threats are worrisome for the particular application (threat modelling). Luckily, more than 20 years of research in the area can help a lot in this endeavour. This talk gives an introduction to privacy preserving machine learning (PPML). We will look at the basic threats against the private training data of a machine learning model, at what defence mechanisms researchers devised to counter them, and what are the research opportunities for the future. We'll then briefly discuss recent techniques for evaluating ML models' security against specific privacy attacks.