Measuring the Leakage of a Black-Box Using Machine Learning - Giovanni Cherubin

Explore a 54-minute talk by Giovanni Cherubin from the Alan Turing Institute on measuring the leakage of a black-box system using machine learning techniques. Delve into the intersection of statistical learning theory, privacy-preserving protocols, and anonymity attacks. Learn about Website Fingerprinting attacks, a major class of traffic analysis threats, and discover practical methods for providing security guarantees to anonymity protocols. Gain insights into improving privacy and anonymity defenses against machine learning-based attacks, including Membership Inference attacks. Understand how Cherubin's background in Classical Studies, Mechatronics, and Computer Engineering informs his approach to confident methods in Machine Learning, such as Conformal Prediction.

Measuring the leakage of a black-box using machine learning: Giovanni Cherubin