ORCID Profile
0000-0002-7823-3061
Current Organisations
Macquarie University
,
Universidade Federal de Minas Gerais
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Publisher: Privacy Enhancing Technologies Symposium Advisory Board
Date: 10-2022
DOI: 10.56553/POPETS-2022-0114
Abstract: 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 ). The approach we suggest brings three principal advantages: it is flexible, allowing for precise quantification and comparison of privacy risks for attacks both known and novel it can be computationally tractable for very large, longitudinal datasets and its results are explainable both to politicians and to the general public. 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 in iduals released longitudinally every year since 2007. These datasets have only very recently (2018–2021) attracted legislation to regulate their privacy — while at the same time continuing to maintain the openness that had been sought in Brazilian society. inep’s reaction to that legislation was the genesis of our project with them. In our conclusions here we share the scientific, technical, and communication lessons we learned in the process.
Publisher: Association for Computing Machinery (ACM)
Date: 13-06-2023
DOI: 10.1145/3589294
Abstract: Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis testing, formally bounds the probability that an attacker may be able to obtain the identity of a user from their representation. 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 complement our theoretical bounds by showing provably good attack algorithms for re-identification that we use to estimate the re-identification risk in the Topics API. We believe this work provides a rigorous and interpretable notion of re-identification risk and a framework to measure it that can be used to inform real-world applications.
Publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
Date: 2020
Publisher: Sociedade Brasileira de Computação - SBC
Date: 31-07-2022
Abstract: 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, based on mathematical concepts from the framework of Quantitative Information Flow (QIF). This brings three principal advantages: flexibility, allowing for precise quantification and comparison of privacy risks for attacks both known and novel computational tractability for very large, longitudinal datasets and explainable results both to politicians and to the general public. We apply our approach to a very large case study: the educational censuses in Brazil, which comprise over 90 attributes of approximately 50 million in iduals released longitudinally every year since 2007.
No related grants have been discovered for Gabriel Nunes.