Longitudinal Datasets

Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata

We present a systematic refactoring of the conventional treatment of privacy analyses, basing it on mathematical concepts from the framework of Quantitative Information Flow (QIF). We apply our approach to a very large case study: the Educational Censuses of Brazil, curated by the governmental agency INEP, which comprise over 90 attributes of approximately 50 million individuals released longitudinally every year since 2007.

Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel H. Nunes