Fluxo de Informação Quantitativo

We analyze the re-identification risk for individual Internet users introduced by the Topics API from the perspective of Quantitative Information Flow (QIF), an information- and decision-theoretic framework. Our model allows a theoretical analysis of both privacy and utility aspects of the API and their trade-off, and we show that the Topics API does have better privacy than third-party cookies. We leave the utility analyses for future work.

We combine Kronecker products, and quantitative information flow, to give a novel formal analysis for the fine-grained verification of utility in complex privacy pipelines. The combination explains a surprising anomaly in the behaviour of utility of privacy-preserving pipelines - that sometimes a reduction in privacy results also in a decrease in utility. We demonstrate our results on a number of common privacy-preserving designs.

We combine Kronecker products, and quantitative information flow, to give a novel formal analysis for the fine-grained verification of utility in complex privacy pipelines. The combination explains a surprising anomaly in the behaviour of utility of privacy-preserving pipelines - that sometimes a reduction in privacy results also in a decrease in utility. We demonstrate our results on a number of common privacy-preserving designs.

We present a summary of the work done in the dissertation A formal quantitative study of privacy in the publication of official educational censuses in Brazil, including its contributions and impacts so far. The dissertation presents 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.

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.

Nesta dissertação, fornecemos um estudo quantitativo completo dos riscos à privacidade na divulgação dos Censos Educacionais Brasileiros oficiais fornecidos anualmente pelo INEP, que é o órgão governamental brasileiro responsável pelo desenvolvimento e manutenção de sistemas de estatísticas educacionais. Mais precisamente, analisamos formalmente os riscos de privacidade em bancos de dados divulgados como microdados, i.e. dados no nível de registro de cada indivíduo, e protegidos pela técnica de desidentificação, i.e. a remoção de informações de identificação direta, como nomes de indivíduos ou números de identificação pessoal.

Neste artigo, estudamos a relação entre privacidade e acurácia no contexto de bases de dados correlacionados.