Processing Text for Privacy: An Information Flow Perspective. - Natasha Fernandes; Mark Dras; Annabelle McIver (FM, 2018)

Author Obfuscation Using Generalised Differential Privacy. - Natasha Fernandes; Mark Dras; Annabelle McIver (CoRR, 2018)

Utility-Preserving Privacy Mechanisms for Counting Queries. - Natasha Fernandes; Lefki Kacem; Catuscia Palamidessi (Models, Languages, and Tools for Concurrent and Distributed Programming, 2019)

Comparing Systems: Max-Case Refinement Orders and Application to Differential Privacy. - Konstantinos Chatzikokolakis 001; Natasha Fernandes; Catuscia Palamidessi (CSF, 2019)

Generalised Differential Privacy for Text Document Processing. - Natasha Fernandes; Mark Dras; Annabelle McIver (POST, 2019)

On Privacy and Accuracy in Data Releases (Invited Paper). - Mário S. Alvim; Natasha Fernandes; Annabelle McIver; Gabriel Henrique Nunes (CONCUR, 2020)

Refinement Orders for Quantitative Information Flow and Differential Privacy. - Konstantinos Chatzikokolakis 001; Natasha Fernandes; Catuscia Palamidessi (J. Cybersecur. Priv., 2020)

Locality Sensitive Hashing with Extended Differential Privacy. - Natasha Fernandes; Yusuke Kawamoto 001; Takao Murakami (ESORICS (2), 2021)

The Laplace Mechanism has optimal utility for differential privacy over continuous queries. - Natasha Fernandes; Annabelle McIver; Carroll Morgan (LICS, 2021)

Differential Privacy for Metric Spaces: Information-Theoretic Models for Privacy and Utility with New Applications to Metric Domains. (Confidentialité différentielle pour les espaces métriques: modèles théoriques de l'information pour la confidentialité et l'utilité avec de nouvelles applications aux domaines métriques). - Natasha Fernandes (2021)

How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk). - Natasha Fernandes; Annabelle McIver; Carroll Morgan (CSL, 2022)

Explaining epsilon in differential privacy through the lens of information theory. - Natasha Fernandes; Annabelle McIver; Parastoo Sadeghi (CoRR, 2022)

Directional Privacy for Deep Learning. - Pedro Faustini; Natasha Fernandes; Annabelle McIver; Mark Dras (CoRR, 2022)

Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata. - Mário S. Alvim; Natasha Fernandes; Annabelle McIver; Carroll Morgan; Gabriel Henrique Nunes (Proc. Priv. Enhancing Technol., 2022)

A novel reconstruction attack on foreign-trade official statistics, with a Brazilian case study. - Danilo Fabrino Favato; Gabriel Coutinho; Mário S. Alvim; Natasha Fernandes (Proc. Priv. Enhancing Technol., 2022)

A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow. - Mário S. Alvim; Natasha Fernandes; Annabelle McIver; Carroll Morgan; Gabriel Henrique Nunes (CCS, 2023)

A Quantitative Information Flow Analysis of the Topics API. - Mário S. Alvim; Natasha Fernandes; Annabelle McIver; Gabriel Henrique Nunes (WPES@CCS, 2023)

Universal optimality and robust utility bounds for metric differential privacy. - Natasha Fernandes; Annabelle McIver; Catuscia Palamidessi; Ming Ding 001 (J. Comput. Secur., 2023)

Quantitative Information Flow Techniques for Studying Optimality in Differential Privacy. - Natasha Fernandes (ACM SIGLOG News, 2023)

The Privacy-Utility Trade-off in the Topics API. - Mário S. Alvim; Natasha Fernandes; Annabelle McIver; Gabriel H. Nunes (CCS, 2024)

Explaining ∊ in Local Differential Privacy Through the Lens of Quantitative Information Flow. - Natasha Fernandes; Annabelle McIver; Parastoo Sadeghi (CSF, 2024)

Formal Privacy Analyses for Open Banking. - Luigi D. C. Soares; Mário S. Alvim; Di Bu; Natasha Fernandes; Yin Liao (SBMF, 2024)

Bayes' capacity as a measure for reconstruction attacks in federated learning. - Sayan Biswas; Mark Dras; Pedro Faustini; Natasha Fernandes; Annabelle McIver; Catuscia Palamidessi; Parastoo Sadeghi (CoRR, 2024)

Comparing privacy notions for protection against reconstruction attacks in machine learning. - Sayan Biswas; Mark Dras; Pedro Faustini; Natasha Fernandes; Annabelle McIver; Catuscia Palamidessi; Parastoo Sadeghi (CoRR, 2025)

Empirical Calibration and Metric Differential Privacy in Language Models. - Pedro Faustini; Natasha Fernandes; Annabelle McIver; Mark Dras (CoRR, 2025)

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation? - Erik Buchholz; Natasha Fernandes; David D. Nguyen; Alsharif Abuadbba; Surya Nepal; Salil S. Kanhere (CoRR, 2025)