Florimond Houssiau
Florimond Houssiau
Alan Turing Institute
Verified email at - Homepage
Cited by
Cited by
When the signal is in the noise: The limits of dif x’s sticky noise
A Gadotti, F Houssiau, L Rocher, Y de Montjoye
arXiv preprint arXiv:1804.06752, 2018
Synthetic Data--what, why and how?
J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ...
arXiv preprint arXiv:2205.03257, 2022
Differentially private compressive k-means
V Schellekens, A Chatalic, F Houssiau, YA De Montjoye, L Jacques, ...
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019
Detrimental network effects in privacy: A graph-theoretic model for node-based intrusions
F Houssiau, P Sapieżyński, L Radaelli, E Shmueli, YA de Montjoye
Patterns 4 (1), 100662, 2023
Evaluating COVID-19 contact tracing apps? Here are 8 privacy questions we think you should ask
YA de Montjoye, F Houssiau, A Gadotti, F Guepin
Computational Privacy Group Blog, 2020
Compressive learning with privacy guarantees
A Chatalic, V Schellekens, F Houssiau, YA De Montjoye, L Jacques, ...
Information and Inference: A Journal of the IMA 11 (1), 251-305, 2022
The risk of re-identification remains high even in country-scale location datasets
A Farzanehfar, F Houssiau, YA de Montjoye
Patterns 2 (3), 100204, 2021
Blogpost: Can We Fight COVID-19 without Re-Sorting to Mass Surveillance
YA De Montjoye, F Houssiau
Computational Privacy Group, 2020
On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data
F Houssiau, L Rocher, YA de Montjoye
Nature communications 13 (1), 29, 2022
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
F Houssiau, J Jordon, SN Cohen, O Daniel, A Elliott, J Geddes, C Mole, ...
arXiv preprint arXiv:2211.06550, 2022
Compressive k-means with differential privacy
V Schellekens, A Chatalic, F Houssiau, YA de Montjoye, L Jacques, ...
SPARS 2019-Signal Processing with Adaptive Sparse Structured Representations …, 2019
A Framework for Auditable Synthetic Data Generation
F Houssiau, SN Cohen, L Szpruch, O Daniel, MG Lawrence, R Mitra, ...
arXiv preprint arXiv:2211.11540, 2022
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice
A Gadotti, F Houssiau, MSMS Annamalai, YA de Montjoye
31st USENIX Security Symposium (USENIX Security 22), 501-518, 2022
MM: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch
F Houssiau, V Schellekens, A Chatalic, SK Annamraju, YA de Montjoye
Security and Trust Management: 18th International Workshop, STM 2022 …, 2023
Web Privacy: A Formal Adversarial Model for Query Obfuscation
F Houssiau, T Liénart, J Hendrickx, YA de Montjoye
IEEE Transactions on Information Forensics and Security 18, 2132-2143, 2023
QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems
AM Cretu, F Houssiau, A Cully, YA de Montjoye
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022
Blogpost: When the signal is in the noise: Ex-ploiting Aircloak’s Diffix anonymization mecha-nism
A Gadotti, F Houssiau, L Rocher, YA de Montjoye
Blogpost: Cambridge Analytica is only the begin-ning and you might have your friends to blame for it
YA de Montjoye, F Houssiau, P Sapieżyński, L Radaelli
Compressive k-means with differential privacy
F Houssiau, YA De Montjoye, V Schellekens, A Chatalic, L Jacques, ...
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