Projects per year
Personal profile
Biography
Natasha is a lecturer in Cybersecurity with special interests in privacy-preserving technologies and mathematical techniques for analysing information leaks in secure systems. She holds an undergraduate degree in Pure Mathematics and Computer Science from the University of Sydney, and a PhD in Computing from Macquarie University and École Polytechnique in France. Natasha has also worked extensively in industry as a software engineer specialising in backend web applications.
Natasha's research interests are in the mathematical foundations of data privacy, particularly involving differential privacy, natural language processing or machine learning. Her work involves probabilistic reasoning using quantitative information flow techniques which derive from information-theoretic principles.
Natasha's research aims at developing mathematical principles to support the analysis of privacy-preserving systems, as well as the development of software tools to support the application of these mathematical techniques in practical engineering scenarios.
Research interests
data privacy, differential privacy, privacy-preserving natural language processing, privacy-preserving machine learning, information flow for privacy and security
Education/Academic qualification
Computing, PhD, Differential Privacy for Metric Spaces: Information-Theoretic Models for Privacy and Utility with New Applications to Metric Domains, Macquarie University
Award Date: 19 Aug 2021
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Collaborations and top research areas from the last five years
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Generalisable Models for Heterogenous Devices Traffic Inference
Gharakheili, H., Batista, G. E., Rui, S., Mishra, D., Sheng, M., Fernandes, N. & Bennett, A.
1/03/24 → 1/09/25
Project: Research
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MQ FCRC: Privacy Analyses for Financial Transaction Data Communications Protocol
Fernandes, N., Liao, Y. & Bu, D.
31/08/23 → 31/08/24
Project: Research
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PRSW23: Google ExploreCSR Grant- Undergraduate Research Skills- 2023
Roberts, M., Ramakrishnan, C. & Fernandes, N.
21/09/22 → 30/06/23
Project: Research
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UAV_TF: Google ExploreCSR Grant-Tensor Flow for UAV-2022
Roberts, M., Ramakrishnan, C., Fernandes, N. & Pappas, C.
30/11/21 → 30/11/22
Project: Research
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Explaining ∈ in local differential privacy through the lens of quantitative information flow
Fernandes, N., McIver, A. & Sadeghi, P., 2024, 2024 IEEE 37th Computer Security Foundations Symposium CSF 2024: proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), p. 419-432 14 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
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A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow
Alvim, M. S., Fernandes, N., McIver, A., Morgan, C. & Nunes, G. H., 2023, CCS '23: proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. New York: Association for Computing Machinery, p. 1718-1731 14 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
2 Citations (Scopus) -
A quantitative information flow analysis of the topics API
Alvim, M. S., Fernandes, N., McIver, A. & Nunes, G. H., 2023, WPES '23: proceedings of the 22nd Workshop on Privacy in the Electronic Society. New York: Association for Computing Machinery, p. 123-127 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
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On the duality of privacy and fairness (extended abstract)
Alvim, M. S., Fernandes, N., Nogueira, B. D., Palamidessi, C. & Silva, T. V. A., 2023, International Conference on AI and the Digital Economy (CADE 2023). London: Institution of Engineering and Technology, 3 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
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Quantitative information flow techniques for studying optimality in differential privacy
Fernandes, N., Jan 2023, In: ACM SIGLOG News. 10, 1, p. 4-22 19 p.Research output: Contribution to journal › Article