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ORCIDAnnabelle McIver
- > Home > Persons > Annabelle McIver
Publications
- 2024
[i34]Sayan Biswas, Mark Dras, Pedro Faustini, Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Parastoo Sadeghi:
Bayes' capacity as a measure for reconstruction attacks in federated learning. CoRR abs/2406.13569 (2024)- [i33]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel H. Nunes:
The Privacy-Utility Trade-off in the Topics API. CoRR abs/2406.15309 (2024) - 2023
- [j34]Natasha Fernandes
, Annabelle McIver, Catuscia Palamidessi, Ming Ding:
Universal optimality and robust utility bounds for metric differential privacy. J. Comput. Secur. 31(5): 539-580 (2023) - [c70]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel Henrique Nunes:
A Novel Analysis of Utility in Privacy Pipelines, Using Kronecker Products and Quantitative Information Flow. CCS 2023: 1718-1731 - [c69]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel Henrique Nunes:
A Quantitative Information Flow Analysis of the Topics API. WPES@CCS 2023: 123-127 - [i31]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel Henrique Nunes:
A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow. CoRR abs/2308.11110 (2023) - [i30]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel Henrique Nunes:
A Quantitative Information Flow Analysis of the Topics API. CoRR abs/2309.14746 (2023) - 2022
- [j33]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel Henrique Nunes:
Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata. Proc. Priv. Enhancing Technol. 2022(4): 378-399 (2022) - [c68]Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Ming Ding:
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy. CSF 2022: 348-363 - [c67]Natasha Fernandes, Annabelle McIver, Carroll Morgan:
How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk). CSL 2022: 2:1-2:14 - [i29]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel Henrique Nunes:
Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata. CoRR abs/2204.13734 (2022) - [i28]Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Ming Ding:
Universal Optimality and Robust Utility Bounds for Metric Differential Privacy. CoRR abs/2205.01258 (2022) - [i27]Natasha Fernandes, Annabelle McIver, Parastoo Sadeghi:
Explaining epsilon in differential privacy through the lens of information theory. CoRR abs/2210.12916 (2022) - [i26]Pedro Faustini, Natasha Fernandes, Annabelle McIver, Mark Dras:
Directional Privacy for Deep Learning. CoRR abs/2211.04686 (2022) - 2021
- [c66]Natasha Fernandes, Annabelle McIver, Carroll Morgan:
The Laplace Mechanism has optimal utility for differential privacy over continuous queries. LICS 2021: 1-12 - [i25]Natasha Fernandes, Annabelle McIver, Carroll Morgan:
The Laplace Mechanism has optimal utility for differential privacy over continuous queries. CoRR abs/2105.07176 (2021) - 2020
- [c65]Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Gabriel Henrique Nunes:
On Privacy and Accuracy in Data Releases (Invited Paper). CONCUR 2020: 1:1-1:18 - 2019
- [c58]Natasha Fernandes, Mark Dras, Annabelle McIver:
Generalised Differential Privacy for Text Document Processing. POST 2019: 123-148 - 2018
- [c57]Natasha Fernandes, Mark Dras, Annabelle McIver:
Processing Text for Privacy: An Information Flow Perspective. FM 2018: 3-21 - [i20]Natasha Fernandes, Mark Dras, Annabelle McIver:
Author Obfuscation Using Generalised Differential Privacy. CoRR abs/1805.08866 (2018) - [i19]Natasha Fernandes, Mark Dras, Annabelle McIver:
Generalised Differential Privacy for Text Document Processing. CoRR abs/1811.10256 (2018)
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