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Natasha Fernandes
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Generalised differential privacy for text document processing
N Fernandes, M Dras, A McIver
Principles of Security and Trust: 8th International Conference, POST 2019 …, 2019
1452019
The laplace mechanism has optimal utility for differential privacy over continuous queries
N Fernandes, A McIver, C Morgan
2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), 1-12, 2021
242021
Comparing systems: Max-case refinement orders and application to differential privacy
K Chatzikokolakis, N Fernandes, C Palamidessi
2019 IEEE 32nd Computer Security Foundations Symposium (CSF), 442-44215, 2019
232019
Locality sensitive hashing with extended differential privacy
N Fernandes, Y Kawamoto, T Murakami
European Symposium on Research in Computer Security, 563-583, 2021
202021
Author obfuscation using generalised differential privacy
N Fernandes, M Dras, A McIver
arXiv preprint arXiv:1805.08866, 2018
162018
Universal optimality and robust utility bounds for metric differential privacy
N Fernandes, A McIver, C Palamidessi, M Ding
Journal of Computer Security 31 (5), 539-580, 2023
132023
On privacy and accuracy in data releases
MS Alvim, N Fernandes, A McIver, GH Nunes
31st International Conference on Concurrency Theory, CONCUR 2020, 1-18, 2020
122020
Processing text for privacy: an information flow perspective
N Fernandes, M Dras, A McIver
Formal Methods: 22nd International Symposium, FM 2018, Held as Part of the …, 2018
122018
Differential privacy for metric spaces: information-theoretic models for privacy and utility with new applications to metric domains
N Fernandes
École Polytechnique Paris; Macquarie University, 2021
102021
Explaining∊ in Local Differential Privacy Through the Lens of Quantitative Information Flow
N Fernandes, A McIver, P Sadeghi
2024 IEEE 37th Computer Security Foundations Symposium (CSF), 419-432, 2024
9*2024
Utility-preserving privacy mechanisms for counting queries
N Fernandes, K Lefki, C Palamidessi
Models, Languages, and Tools for Concurrent and Distributed Programming …, 2019
92019
Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata
MS Alvim, N Fernandes, A McIver, C Morgan, GH Nunes
arXiv preprint arXiv:2204.13734, 2022
82022
A quantitative information flow analysis of the topics API
MS Alvim, N Fernandes, A McIver, GH Nunes
Proceedings of the 22nd Workshop on Privacy in the Electronic Society, 123-127, 2023
72023
A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow
MS Alvim, N Fernandes, A McIver, C Morgan, GH Nunes
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications …, 2023
72023
Refinement orders for quantitative information flow and differential privacy
K Chatzikokolakis, N Fernandes, C Palamidessi
Journal of Cybersecurity and Privacy 1 (1), 40-77, 2020
62020
A novel reconstruction attack on foreign-trade official statistics, with a Brazilian case study
D Fabrino Favato, G Coutinho, MS Alvim, N Fernandes
arXiv e-prints, arXiv: 2206.06493, 2022
4*2022
Directional privacy for deep learning
P Faustini, N Fernandes, S Tonni, A McIver, M Dras
arXiv preprint arXiv:2211.04686, 2022
32022
A novel framework for author obfuscation using generalised differential privacy
N Fernandes
Macquarie University, 2017
22017
The Privacy-Utility Trade-off in the Topics API
MS Alvim, N Fernandes, A McIver, GH Nunes
Proceedings of the 2024 on ACM SIGSAC Conference on Computer and …, 2024
12024
Bayes' capacity as a measure for reconstruction attacks in federated learning
S Biswas, M Dras, P Faustini, N Fernandes, A McIver, C Palamidessi, ...
arXiv preprint arXiv:2406.13569, 2024
12024
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