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Quantitative Information Flow
A Quantitative Information Flow Analysis of the Topics API
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.
Mário S. Alvim
,
Natasha Fernandes
,
Annabelle McIver
,
Gabriel H. Nunes
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A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow
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.
Mário S. Alvim
,
Natasha Fernandes
,
Annabelle McIver
,
Carroll Morgan
,
Gabriel H. Nunes
PDF
Cite
DOI
A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow
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.
Mário S. Alvim
,
Natasha Fernandes
,
Annabelle McIver
,
Carroll Morgan
,
Gabriel H. Nunes
A formal quantitative study of privacy in the publication of official educational censuses in Brazil
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.
Mário S. Alvim
,
Annabelle McIver
,
Gabriel H. Nunes
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Code
DOI
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
PDF
Cite
Code
Video
DOI
A formal quantitative study of privacy in the publication of official educational censuses in Brazil
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.
Gabriel H. Nunes
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DOI
On Privacy and Accuracy in Data Releases
In this paper we study the relationship between privacy and accuracy in the context of correlated datasets.
Mário S. Alvim
,
Natasha Fernandes
,
Annabelle McIver
,
Gabriel H. Nunes
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