This talk will provide an overview of differential privacy, one of the leading approaches to data privacy, with a focus on what guarantees it provides, why these guarantees are meaningful and necessary, how to understand and think about its “privacy parameters,” and when to use (or not!) differential privacy. While this talk does not pretend to provide any definitive answer to these questions, the hope is that it can serve as a basis for practitioners and policymakers interested in the application and deployment of differential privacy.
Clément Canonne is a Senior Lecturer in the School of Computer Science of the University of Sydney, an ARC DECRA Fellow, and a 2023 NSW Young Tall Poppy. He obtained his Ph.D. in 2017 from Columbia University, before joining Stanford as a Motwani Postdoctoral Fellow, then IBM Research as a Goldstine Postdoctoral Fellow. His research interests span distribution testing and learning theory; focusing, in particular, on differential privacy, and the computational aspects of learning and statistical inference subject to resource or information constraints.