| Synthetic Data--what, why and how? J Jordon, L Szpruch, F Houssiau, M Bottarelli, G Cherubin, C Maple, ... arXiv preprint arXiv:2205.03257, 2022 | 150 | 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 | 40* | 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 | 33 | 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 |
| 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 |
| 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 | 19 | 2022 |
| 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 |
| 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 |
| 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 | 13 | 2022 |
| 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 |
| 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 | 8 | |
| 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 | 4 | 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 |
| 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 | | 2024 |
| 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 |
| 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 International Workshop on Security and Trust Management, 117-135, 2022 | | 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 | | 2018 |
Yves-Alexandre de MontjoyeAssociate Professor at Imperial College LondonVerified email at mit.edu
