Privacidade

In this work, we present a new theoretical framework to measure re-identification risk in such user representations. As an application, we show how our framework is general enough to model important real-world applications such as the Chrome’s Topics API for interest-based advertising.

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.

In this work, we present a new theoretical framework to measure re-identification risk in such user representations. As an application, we show how our framework is general enough to model important real-world applications such as the Chrome’s Topics API for interest-based advertising.

In this work, we present a new theoretical framework to measure re-identification risk in such user representations. As an application, we show how our framework is general enough to model important real-world applications such as the Chrome’s Topics API for interest-based advertising.

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.

A divulgação de dados estatísticos, incluindo microdados, é objeto de estudo e de preocupação da comunidade científica há mais de quatro décadas.

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.