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Natasha Fernandes
Natasha Fernandes
Dirección de correo verificada de mq.edu.au
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Año
Generalised differential privacy for text document processing
N Fernandes, M Dras, A McIver
Principles of Security and Trust: 8th International Conference, POST 2019 …, 2019
1252019
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
222019
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
212021
Locality sensitive hashing with extended differential privacy
N Fernandes, Y Kawamoto, T Murakami
European Symposium on Research in Computer Security, 563-583, 2021
192021
Author obfuscation using generalised differential privacy
N Fernandes, M Dras, A McIver
arXiv preprint arXiv:1805.08866, 2018
142018
On privacy and accuracy in data releases
MS Alvim, N Fernandes, A McIver, GH Nunes
31st International Conference on Concurrency Theory (CONCUR 2020), 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
112018
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
102023
Utility-preserving privacy mechanisms for counting queries
N Fernandes, K Lefki, C Palamidessi
Models, Languages, and Tools for Concurrent and Distributed Programming …, 2019
102019
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
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
82021
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
62022
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 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
42023
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
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
22023
A novel framework for author obfuscation using generalised differential privacy
N Fernandes
Macquarie University, 2017
22017
4.2 Metrics for anonymization of unstructured datasets
L Belkadi, M De Cock, N Fernandes, K Lee, C Lohr, A Nautsch, L Sion, ...
Privacy in Speech and Language Technology, 73, 0
1
The Privacy-Utility Trade-off in the Topics API
MS Alvim, N Fernandes, A McIver, GH Nunes
arXiv preprint arXiv:2406.15309, 2024
2024
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
2024
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20