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Yves-Alexandre de Montjoye

Yves-Alexandre de Montjoye is an Associate Professor at Imperial College London. He currently is a Special Adviser on AI and Data Protection to EC Justice Commissioner Reynders and a Parliament-appointed expert to the Belgian Data Protection Agency (APD-GBA). In 2018-2019, he was a Special Adviser to EC Competition Commissioner Vestager co-authoring the Competition Policy for the Digital Era report. His research has been published in Science and Nature Communications and has enjoyed wide media coverage (BBC, CNN, New York Times, Wall Street Journal, Harvard Business Review, etc.). His work on the shortcomings of anonymization has appeared in reports of the World Economic Forum, FTC, European Commission, and the OECD. Yves-Alexandre worked for the Boston Consulting Group and acted as an expert for both the Bill and Melinda Gates Foundation and the United Nations. He received his PhD from MIT in 2015 and obtained, over a period of 6 years, an M.Sc. from UCLouvain in Applied Mathematics, an M.Sc. (Centralien) from École Centrale Paris, an M.Sc. from KULeuven in Mathematical Engineering as well as his B.Sc. in engineering from UCLouvain.

News

Publications

  • Stevanoski, B., Cretu, A.-M., and de Montjoye Y. A. QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems. ACM Conference on Computer and Communications Security (ACM CCS 2024) (2024).

  • Meeus, M., Jain, S., Rei, M. and de Montjoye Y. A. Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models. 33rd USENIX Security Symposium (USENIX Security 2024) (2024).
    Selected Press: Le Monde

  • Meeus, M., Shilov, I., Faysse, M. and de Montjoye Y. A. Copyright Traps for Large Language Models. 41st International Conference on Machine Learning (ICML 2024) (2024).
    Selected Press: MIT Technology Review, Nature News

  • 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).

  • Guan, V., Guépin, F., Cretu, A.-M., and de Montjoye Y. A. A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data. Proceedings on Privacy Enhancing Technologies 2024(4) (PoPETS 2024) (2024).

  • Cretu, A.-M., Jones, Daniel, de Montjoye Y. A, and Tople, Shruti. Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting. In Proceedings on Privacy Enhancing Technologies 2024(3), 407–430. (2024).

  • Cretu, A.-M., Guépin, F., and de Montjoye Y. A. Correlation inference attacks against machine learning models. Science Advances, 2024 (2024).

  • Cretu, A.-M.*, Rusu, Miruna*, and de Montjoye Y. A. Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks. In Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy (CODASPY ’24), June 19–21, 2024, Porto, Portugal. ACM, New York, NY, USA. (2024).

  • Meeus, M., Shilov, I., and de Montjoye Y. A. Mosaic Memory: Fuzzy Duplication in Copyright Traps for Large Language Models. ArXiv preprint (2024).

  • Guépin, F., Krčo, N., Meeus, M. and de Montjoye Y. A. Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models. ArXiv preprint (2024).

  • Guépin, F., Meeus, M., Cretu, A.-M., and de Montjoye Y. A. Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data. 18th DPM International Workshop on Data Privacy Management, Sept 2023, The Hague (2023).

  • Meeus, M., Guépin, F., Cretu, A.-M., and de Montjoye Y. A. Achilles’ Heels: Vulnerable Record Identification in Synthetic Data Publishing. 28th European Symposium on Research in Computer Security (ESORICS), Sept 2023, The Hague (2023).

  • Meeus, M., Jain, S., and de Montjoye Y. A. Concerns about using a digital mask to safeguard patient privacy. Matters Arising in Nature Medicine, July 2023 (2023).

  • Jain, S., Cretu, A.-M., Cully, A. and de Montjoye Y. A. Deep perceptual hashing algorithms with hidden dual purpose: when client-side scanning does facial recognition. 2023 IEEE Symposium on Security and Privacy (SP) (2023).
    Selected Press: Imperial College London News

  • Rocher, L., Tournier, A. J., & de Montjoye, Y. A. Adversarial competition and collusion in algorithmic markets. Nature Machine Intelligence (2023).
    Selected Press: POLITICO Pro Fair Play, POLITICO Pro Morning Tech

  • 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

  • Tournier, A. J., & de Montjoye, Y. A. Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data. Science Advances (2022).

  • 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).

  • Jain, S.*, Cretu, A.-M.*, and de Montjoye Y. A. Adversarial Detection Avoidance Attacks: Evaluating the robustness of perceptual hashing-based client-side scanning. 31st USENIX Security Symposium (2022).
    Selected Press: Imperial College London News

  • Cretu, A.-M., Monti, F., Marrone, S., Dong, X., Bronstein, M. and de Montjoye Y. A. Interaction data are identifiable even across long periods of time. Nature Communications (13), 313 (2022).
    Selected Press: Science News, RFI

  • 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).

  • Oehmichen, A., Jain, S., Gadotti, A., & de Montjoye, Y. A. OPAL: High performance platform for large-scale privacy-preserving location data analytics. 2019 IEEE International Conference on Big Data (Big Data) (pp. 1332-1342) (2019).

  • 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

  • Rocher, L., Hendrickx, J. M., & de Montjoye, Y. A. Estimating the success of re-identifications in incomplete datasets using generative models. Nature communications, 10 (1), 3069 (2019).
    Selected Press: New York Times, Guardian, CNBC, The Telegraph, TechCrunch, Technology Review, New Scientist, Gizmodo, Scientific American, RT, Forbes, El Pais (ES), Sueddeutsche Zeitung (DE), Le Soir (FR), La Libre (FR), L'Echo (FR), De Morgen (NL)

  • 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).

  • Jain, S., Bensaid, E., & de Montjoye, Y. A. UNVEIL: Capture and Visualise WiFi Data Leakages. The World Wide Web Conference (pp. 3550-3554) (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).

  • de Montjoye Y. A., Farzanehfar A., Hendrickx J., Rocher L. Solving Artifical Intelligence's Privacy Problem. Field Actions Science Reports. The journal of field actions (Special Issue 17) pp 80-83 (2017).