Privacy

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.

In this thesis, we provide a thorough quantitative study of privacy risks in the release of the official Brazilian Educational Censuses provided annually by INEP, which is Brazil’s governmental agency responsible for the development and maintenance of educational statistics systems. More precisely, we formally analyze privacy risks in databases released as microdata, i.e. data at each individual’s record level, and protected by the technique of de-identification, i.e. the removal of direct identifying information such as the individuals’ names or personal identification numbers.

Quantitative Information Flow assessment of vulnerability for microdata datasets using Bayes Vulnerability.

In this paper we study the relationship between privacy and accuracy in the context of correlated datasets.

Talk in Portuguese.