Abstract: The data society we live in offers unprecedented opportunities to exploit available data. The goal of _data mining_ is precisely to extract useful information from available data and assist in e.g., decision-making or event-prediction. In doing so, available data usually contain sensitive information about individuals, and releasing even _aggregate_ information about a set of individuals may seriously compromise their individual privacy. In this context, _differential privacy_ has been established as the de facto framework for mining sensitive data in a privacy-aware manner.
In this talk I will give a short (and gentle) introduction to differential privacy. I will approach it from both a theoretical and a practical perspective and conclude discussing some recent developments.
Bio: Federico Olmedo is a full-time professor in the Computer Science Department at the University of Chile. Before joining the University of Chile, he spent three years as a postdoctoral researcher in the Modeling and Verification Group at the RTWH Aachen University, Germany, and in 2014 he earned his PhD degree in Computer Science from the Technical University of Madrid, Spain. His research interests are programming languages, in particular, probabilistic program verification and language-based security.
Date: August 31st, 2018
Venue: Ada Lovelace Auditorium, Computer Sciences Departament, University of Chile