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, 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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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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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
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A novel reconstruction attack on foreign-trade official statistics, with a Brazilian case study
Favato, D. F., Coutinho, G., Alvim, M. S. & Fernandes, N., 2022, In: Proceedings on Privacy Enhancing Technologies. 2022, 4, p. 608-625 18 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata
Alvim, M. S., Fernandes, N., McIver, A., Morgan, C. & Nunes, G. H., 2022, In: Proceedings on Privacy Enhancing Technologies. 2022, 4, p. 378-399 22 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile6 Downloads (Pure) -
How to develop an intuition for risk. . . and other invisible phenomena
Fernandes, N., McIver, A. & Morgan, C., Feb 2022, 30th EACSL Annual Conference on Computer Science Logic, CSL 2022. Manea, F. & Simpson, A. (eds.). Wadern, Germany: Dagstuhl Publishing, p. 1-14 14 p. 2. (Leibniz International Proceedings in Informatics, LIPIcs; vol. 216).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
Open AccessFile12 Downloads (Pure) -
Universal optimality and robust utility bounds for metric differential privacy
Fernandes, N., McIver, A., Palamidessi, C. & Ding, M., 2022, 2022 IEEE 35th Computer Security Foundations Symposium (CSF 2022): proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), p. 348-363 16 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review