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 1) give a short and gentle introduction to differential privacy, and 2) present a concrete application example to learning analytics.
Federico Olmedo, Assistant Professor in Computer Science Department, University of Chile. His research interest are the semantics and verification of programs in general, and in particular, in the verification of probabilistic programs, program semantics and verification, probabilistic programming, theorem provers and language-based security.
WHEN AND WHERE
September 7, 18.30 hrs.
Instituto Milenio Fundamentos de los Datos.
Edificio de Innovación Campus San Joaquín Universidad Católica.