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
Fingerprint
Dive into the research topics where Natasha Fernandes is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Projects
- 1 Finished
-
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 AccessFile2 Downloads (Pure) -
Locality sensitive hashing with extended differential privacy
Fernandes, N., Kawamoto, Y. & Murakami, T., 2021, Computer Security – ESORICS 2021: 26th European Symposium on Research in Computer Security Darmstadt, Germany, October 4–8, 2021, Proceedings, Part II. Bertino, E., Shulman, H. & Waidner, M. (eds.). Cham, Switzerland: Springer, Springer Nature, p. 563-583 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12973 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
-
Refinement orders for quantitative information flow and differential privacy
Chatzikokolakis, K., Fernandes, N. & Palamidessi, C., Mar 2021, In: Journal of Cybersecurity and Privacy. 1, 1, p. 40-77 38 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
The Laplace Mechanism has optimal utility for differential privacy over continuous queries
Fernandes, N., McIver, A. & Morgan, C., 2021, 36th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2021. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 12 p. (Proceedings - Symposium on Logic in Computer Science; vol. 2021-June).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
1 Citation (Scopus) -
On privacy and accuracy in data releases
Alvim, M. S., Fernandes, N., McIver, A. & Nunes, G. H., Aug 2020, 31st International Conference on Concurrency Theory: CONCUR 2020, September 1–4, 2020, Vienna, Austria (Virtual Conference). Konnov, I. & Kovács, L. (eds.). Saarbrücken/Wadern, Germany: Dagstuhl Publishing, 18 p. 1. (Leibniz International Proceedings in Informatics, LIPIcs; vol. 171).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
Open AccessFile6 Downloads (Pure)