Team - Computational Privacy Group, Imperial College London

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 Ã‰cole Centrale Paris, an M.Sc. from KULeuven in Mathematical Engineering as well as his B.Sc. in engineering from UCLouvain.

News

Jul 14, 2023 – CPG at CNIL Privacy Research Day

Nov 10, 2022 – CPG at ACM CCS 2022

Aug 19, 2022 – New paper “Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data” published in Science Advances

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”

Aug 10, 2022 – CPG at USENIX Security 2022

Apr 11, 2022 – Our work was featured in John Oliver’s Last Week Tonight

Jan 27, 2022 – Yves-Alexandre de Montjoye at Digital Regulation Co-operation Forum (DRCF) E2EE roundtable

Jan 25, 2022 – New paper “Interaction data are identifiable even across long periods of time” published in Nature Communications

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

Nov 19, 2021 – Contributed talk at the ACM CCS Privacy-Preserving Machine Learning workshop (PPML 2021)

Nov 5, 2021 – Ana-Maria Crețu and Shubham Jain at the Conference on Applied Machine Learning in Information Security (CAMLIS)

Sep 28, 2021 – New USENIX Security paper: “Adversarial Detection Avoidance Attacks: Evaluating the robustness of perceptual hashing-based client-side scanning”

Jun 9, 2021 – CPG paper “Unique in the Crowd” mentioned in MTD to the US district court for the Central District of California on e-scooters 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?

Jan 15, 2020 – WEF 2020 panel: Can AI and Privacy co-exist?

Jul 23, 2019 – New Nature Communication paper on the risks of re-identification in incomplete datasets

Feb 14, 2019 – New WWW Demo Paper: "UNVEIL: Capture and Visualise WiFi Data Leakages"

Jul 13, 2018 – 'Data is a fingerprint': why you aren't as anonymous as you think online

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?

Feb 16, 2018 – Solving AI's Privacy Problem

Publications

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).

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

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).

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).