| 2026 | FOSSACS | Composition Theorems for f-Differential Privacy. | Natasha Fernandes, Annabelle McIver, Parastoo Sadeghi |
| 2026 | SP | Inspectable AI for Science: A Research Object Approach to Generative AI Governance. | Ruta Binkyte, Sharif Abuaddba, Chamikara Mahawaga Arachchige, Ming Ding, Natasha Fernandes, Mario Fritz |
| 2024 | CCS | The Privacy-Utility Trade-off in the Topics API. | Mrio S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel H. Nunes |
| 2023 | CCS | A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow. | Mrio S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel Henrique Nunes |
| 2022 | CSL | How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk). | Natasha Fernandes, Annabelle McIver, Carroll Morgan |
| 2021 | ESORICS | Locality Sensitive Hashing with Extended Differential Privacy. | Natasha Fernandes, Yusuke Kawamoto, Takao Murakami |
| 2021 | LICS | The Laplace Mechanism has optimal utility for differential privacy over continuous queries. | Natasha Fernandes, Annabelle McIver, Carroll Morgan |
| 2020 | CONCUR | On Privacy and Accuracy in Data Releases (Invited Paper). | Mrio S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel Henrique Nunes |
| 2018 | FM | Processing Text for Privacy: An Information Flow Perspective. | Natasha Fernandes, Mark Dras, Annabelle McIver |