Team - Computational Privacy Group, Imperial College London

Florimond Houssiau

Email: florimond AT imperial.ac.uk

Hi! I’m a PhD student in the Computational Privacy Group at Imperial College. I graduated in 2017 with a Master of Engineering in applied mathematics at the Université catholique de Louvain, in Belgium. I studied a range of topics from mathematics to computer science, with a particular focus on discrete math, algorithms, and statistical modelling. I first started working on privacy during an internship at MIT.

Research-wise, I’ve been interested in a range of questions in adversarial privacy, ranging from query obfuscation to protect privacy in Web search queries to large-scale network-based attacks. I interned at Google in 2019, where I worked on differential privacy. I’ve also been a teaching assistant in machine learning and privacy engineering here at Imperial.

News

Nov 10, 2022 – CPG at ACM CCS 2022

Aug 10, 2022 – New USENIX paper: “Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice”

Jan 10, 2022 – New Nature Communication paper: “On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data”

Mar 12, 2021 – “The risk of re-identification remains high even in country-scale location datasets” published in Cell Patterns

Apr 2, 2020 – Evaluating COVID-19 contact tracing apps? Here are 8 privacy questions we think you should ask.

Mar 21, 2020 – Can we fight COVID-19 without resorting to mass surveillance?

Apr 24, 2018 – When the signal is in the noise: Exploiting Aircloak's Diffix anonymization mechanism

Mar 29, 2018 – Cambridge Analytica is only the beginning. Should you blame your friends for it?

Publications

Gadotti, A., Rocher, L., Houssiau, F., Cretu, A.-M., and de Montjoye Y. A. Anonymization: The imperfect science of using data while preserving privacy. Science Advances, 2024 (2024).

Houssiau, F., Liénart, T., Hendrickx, J. and de Montjoye Y. A. Web privacy: a Formal Adversarial Model for Query Obfuscation. IEEE Transactions on Information Forensics and Security (2023).

Houssiau, F., Sapieżyński, P., Radaelli, L., Shmueli, E. and de Montjoye Y. A. Detrimental network effects in privacy: A graph-theoretic model for node-based intrusions. Patterns, 4(1) (2023).

Houssiau, F., Schellekens, V., Chatalic, A., Annamraju, S. K. and de Montjoye Y. A. M2M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch. The 18th International Workshop on Security and Trust Management (2022).

Cretu, A.-M.*, Houssiau, F.*, Cully, A., and de Montjoye Y. A. QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS '22). Association for Computing Machinery, New York, NY, USA, 623–637. (2022).
Selected Press: Imperial College London

Gadotti A., Houssiau F., Annamalai M.S.M.S., & de Montjoye, Y. A. Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice. 31st USENIX Security Symposium (2022).

Houssiau, F., Rocher, L. and de Montjoye Y. A. On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data. Nature Communications, 2022 (2022).

Gadotti A., Houssiau F., Rocher L., Livshits B., de Montjoye Y. A. When the signal is in the noise: Exploiting Diffix's Sticky Noise. 28th USENIX Security Symposium (2019).
Selected Press: TechCrunch, Wall Street Journal

Schellekens V., Chatalic A., Houssiau F., de Montjoye Y. A., Jacques L., Gribonval R. Differentially Private Compressive K-means. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019).

Houssiau F., Radaelli L., Sapiezynsky P., Shmueli E., de Montjoye Y. A. Quantifying surveillance in the networked age: Node-based intrusions and group privacy. (2018).