Keynote Speaker

Abstract: Machine learning (ML) models are often trained on personal or otherwise sensitive data. For example, ML models are trained on health data supplied by smartphones or sensitive data coming from IoT sensors. In several jurisdictions, the legal framework for the publication and disclosure of personal data is being extended to ML. This is based on the implicit assumption that disclosing a trained ML model poses a similar privacy risk to the personal data used to train it as directly publishing those data. However, with a trained model, it is necessary to carry out a privacy attack to draw inferences from the training data. In this talk, I examine the main families of privacy attacks against predictive and generative ML, including membership inference attacks (MIAs), property inference attacks, and reconstruction attacks. This analysis shows that most of these attacks appear to be less effective in the real world than might be suggested by a prima facie interpretation of the relevant literature. The talk will conclude by highlighting the role of privacy attacks to assess machine unlearning for privacy, and the potential clash between machine unlearning and blockchain-recorded ML.

Short biography: Josep Domingo-Ferrer received BSc-MSc and PhD degrees in Computer Science (Universitat Autònoma de Barcelona), a BSc-MSc in Mathematics (UNED), and an MA in Philosophy (Université de Paris Nanterre). He is a University Professor of Computer Science and an ICREA-Acadèmia Research Professor at Universitat Rovira i Virgili in Tarragona, Catalonia, where he also leads CYBERCAT (Center for Cybersecurity Research of Catalonia). His research interests include privacy, data security, trustworthy machine learning, and ethics in information technology. He is an IEEE Fellow, an ACM Distinguished Scientist, an elected member of Academia Europaea, an elected member of the International Statistical Institute, a Fellow of Institut d'Estudis Catalans (Catalan national academy), and a Chevalier (Knight) dans l'Ordre des Palmes Académiques (France).