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.



  • 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).