I am a postdoc in the SPRING Lab at EPFL, headed by Prof. Carmela Troncoso. My position is funded by the CYD Distinguished Postdoctoral Fellowship of the Cyber-Defense Campus, where my main collaborator is Dr. Raphael Meier.

I completed my PhD in the Computational Privacy Group at Imperial College London, advised by Dr. Yves-Alexandre de Montjoye. My research lies at the intersection between machine learning, privacy, and security. I study privacy and security vulnerabilities in data processing technologies: machine learning models, query-based systems, and perceptual hashing-based client-side scanning, through the lens of automated attacks. Through a rigorous study of privacy vulnerabilities, my research can inform the design of principled countermeasures allowing to prevent them and, ultimately, to use data safely.

News

23/08/2024: New paper QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems accepted at ACM CCS 2024! Joint work with Bozhidar Stevanoski and Yves-Alexandre de Montjoye.

17/07/2024: Our review paper on Anonymization: The imperfect science of using data while preserving privacy has been published in Science Advances. Joint work with Andrea Gadotti, Luc Rocher, Florimond Houssiau and Yves-Alexandre de Montjoye.

20/06/2024: Our paper Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks received the Best Paper Award at the ACM CODASPY ‘24 conference! 🏆 Miruna Rusu, co-first author on the paper, presented the work in Porto, Portugal.

08/06/2024: New paper A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data accepted in PoPETS 2024! Joint work with Vincent Guan, Florent Guépin and Yves-Alexandre de Montjoye.

05/06/2024: New paper Correlation inference attacks against machine learning models accepted in Science Advances! Joint work with Florent Guépin and Yves-Alexandre de Montjoye.

More news here.

Publications

Peer-reviewed articles

* denotes joint first authorship.

1. Bozhidar Stevanoski, Cretu A.-M. and de Montjoye Y.-A. QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems. To appear in ACM CCS 2024. [Extended arXiv version] [Code]
   • Presented as a poster at the TPDP '24 workshop in Boston, MA, USA.
2. Andrea Gadotti, Luc Rocher, Florimond Houssiau, Crețu A.-M. and de Montjoye Y.-A. Anonymization: The imperfect science of using data while preserving privacy. In Science Advances, 2024. [Paper]
3. Crețu A.-M.*, Guépin F.* and de Montjoye Y.-A. Correlation inference attacks against machine learning models. In Science Advances, 2024. [Paper] [Code]
4. Guan, V.*, Guépin F.*, Crețu A.-M. and de Montjoye Y.-A. A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data. In Proceedings on Privacy Enhancing Technologies 2024(4) (PoPETS 2024). [Paper]
5. Cretu A.-M., Jones, D., de Montjoye Y.-A., and Tople, S. Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting. In Proceedings on Privacy Enhancing Technologies 2024(3) (PoPETS 2024). [Paper] [Code]
6. Cretu A.-M.*, Rusu, M.*, 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). [Paper] [Extended arXiv version]
   • Recipient of a Best paper award at the ACM CODASPY '24 conference in Porto, Portugal.
7. Cretu A.-M. Evaluating privacy and robustness in modern data processing systems. PhD Thesis, Imperial College London (2023). [Thesis]
8. Guépin, F.*, Meeus, M.*, Crețu A.-M., and de Montjoye Y.-A. Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data. In 18th DPM International Workshop on Data Privacy Management (DPM 2023). [Paper] [Code]
9. Meeus, M.*, Guépin, F.*, Crețu A.-M., and de Montjoye Y.-A. Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing. In 28th European Symposium on Research in Computer Security (ESORICS 2023). [Paper] [Code]
10. Jain S., Crețu A.-M., Cully, A. and de Montjoye Y.-A. Deep perceptual hashing algorithms with hidden dual-purpose: when client-side scanning does facial recognition. In 2023 IEEE Symposium on Security and Privacy (SP). [Paper]
    • Featured in Imperial News and Le soir.
    • Cited as evidence in the Open Letter from Security and Privacy Researchers in relation to the Online Safety Bill.
11. Crețu 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). [Paper] [Extended arXiv version] [Code]
12. Crețu 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). [Paper]
    • Presented at the ACM CCS Privacy Preserving Machine Learning 2021 workshop (PPML 2021). Contributed talk.
    • Presented at the NeurIPS Privacy and Machine Learning 2021 workshop (PriML 2021).
    • Featured in TechCrunch and Science News.
13. Jain S.*, Crețu 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 (USENIX Security 22) [Paper] [Extended arXiv version] [Code]
    • Cited by Ofcom (UK's communications regulator) in their Overview of Perceptual Hashing Technology report.
    • Presented at the NeurIPS Privacy and Machine Learning 2021 workshop (PriML 2021).
    • Presented at the Conference on Applied Machine Learning for Information Security 2021 (CAMLIS 2021). Oral presentation.
    • Presented as a talk at the 14th Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETS 2021).
    • Featured in Imperial College London News and Le soir.
14. Kocijan V., Camburu O.-M., Crețu A.-M., Yordanov Y., Blunsom P. and Lukasiewicz T. WikiCREM: A Large Unsupervised Corpus for Coreference Resolution. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (2019) [Paper]
15. Kocijan V., Crețu A.-M., Camburu O.-M., Yordanov Y. and Lukasiewicz T. A Surprisingly Robust Trick for the Winograd Schema Challenge. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) [Paper]

Preprints

Awards and scholarships

• I am a recipient of the Cyber-Defense Campus (CYD) Distinguished Postdoctoral Fellowship, which generously supports my postdoctoral position in the SPRING Lab at EPFL.
• I am a recipient of the USENIX '22 Diversity grant, which generously supported my trip to the conference in Boston.
• I am a recipient of the EPFL Excellence Fellowship (awarded to students with outstanding academic records), which generously supported my MSc studies at EPFL.
• My studies in France were supported by a competitive 2-year full scholarship from the Fondation Odon Vallet and by a 2.5-year scholarship from the Fondation de l'Ecole Polytechnique.
• I was born and grew up in Buzău, Romania. There, I participated in many mathematics contests, including the Romanian National Olympiad, where I won one gold medal, three silver medals, and one bronze medal.