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+type: past
+date: 2024-03-26T15:00:00+1:00
+speaker: Giovanni Cherubin
+affiliation: Microsoft
+title: "A Deep Dive into the Privacy of Machine Learning"
+bio: "Giovanni Cherubin is a Senior Researcher at Microsoft (Cambridge) working with the Microsoft Response Centre (MSRC). Before joining Microsoft, he held research positions at the Alan Turing Institute and EPFL, and he obtained a PhD in Machine Learning and Cyber Security from Royal Holloway University of London. 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). Some of his work on security and machine learning has been recognised with best student paper awards (SLDS15, PETS17), distinguished paper (USENIX22), and with a USENIX Internet Defense Prize (2022)."
+abstract: "The hope to train machine learning models whilst ensuring the privacy of their training data is well 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, >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."
+youtube: i6h_M2eamOk
+
+---