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Datasets Longitudinais
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
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