Synthetic Data--what, why and how? J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ...
arXiv preprint arXiv:2205.03257, 2022
172 2022 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
41 * 2018 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
34 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
27 2019 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
23 2022 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), 2023
21 * 2023 The risk of re-identification remains high even in country-scale location datasets A Farzanehfar, F Houssiau, YA de Montjoye
Patterns 2 (3), 2021
21 2021 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
17 2022 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
14 2022 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
14 2020 Synthetic data–what, why and how?(2022) J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ...
arXiv preprint arXiv:2205.03257, 0
11 Blogpost: Can We Fight COVID-19 without Re-Sorting to Mass Surveillance YA De Montjoye, F Houssiau
Computational Privacy Group, 2020
10 2020 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
9 2022 Anonymization: The imperfect science of using data while preserving privacy A Gadotti, L Rocher, F Houssiau, AM Creţu, YA de Montjoye
Science Advances 10 (29), eadn7053, 2024
5 2024 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
5 2022 Synthetic Data–what, why and how?,. arXiv J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ...
arXiv preprint arXiv:2205.03257, 2022
5 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
4 2019 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
1 2023 Transparent Decisions: Selective Information Disclosure To Generate Synthetic Data C Gavidia-Calderon, S Harris, M Hauru, F Houssiau, C Maple, I Stenson, ...
Data Engineering, 51, 2023
2023 M F Houssiau, V Schellekens, A Chatalic, SK Annamraju, YA de Montjoye
International Workshop on Security and Trust Management, 117-135, 2022
2022 
Yves-Alexandre de MontjoyeAssociate Professor at Imperial College LondonBestätigte E-Mail-Adresse bei imperial.ac.uk
