Pascal Berrang

IT Security & Privacy (Blockchain, AI, Medical Data)

Birmingham, UK and Saarbr√ľcken, Germany

I am an Assistant Professor in Computer Science ‚Äď Computer Security at the University of Birmingham, UK.

My research interests are in the field of IT Security & Privacy with a focus on Health Data, Blockchain Technology, and Artificial Intelligence & Machine Learning.

Previously, I was a freelance researcher & consultant, specialising in blockchain technology (e.g., Nimiq). Prior to that, I completed my PhD in the Information Security and Cryptography Group at Saarland University under supervision of Michael Backes. My PhD thesis has the title Quantifying and Mitigating Privacy Risks in Biomedical Data. My thesis recently received the Dr. Eduard-Martin award 2019 for the best PhD thesis in the category in mathematics and computer science. I obtained my Bachelor degree from Saarland University in 2013, before joining the Graduate School of Computer Science there.

research areas

  • Security and Privacy of Machine Learning
  • Security and Privacy of Blockchain Technology
  • Security and Privacy of Health Data

If you’re a student interested in pursuing a PhD in one of these topics, don’t hesitate contacting me!

selected publications

    Quantifying Privacy Risks of Prompts in Visual Prompt Learning
    Wu, Yixin, Wen, Rui, Backes, Michael,  Berrang, Pascal, Humbert, Mathias, Shen, Yun, and Zhang, Yang
    In Proceedings of the 33rd USENIX Security Symposium (Security) 2024
  2. NDSS
    Accountable Javascript Code Delivery
    Esiyok, Ilkan,  Berrang, Pascal, Cohn-Gordon, Katriel, and Kuennemann, Robert
    In Proceedings of the 30th Annual Network and Distributed System Security Symposium (NDSS) 2023
  3. WWW
    On How Zero-Knowledge Proof Blockchain Mixers Improve, and Worsen User Privacy
    Wang, Zhipeng, Chaliasos, Stefanos, Qin, Kaihua, Zhou, Liyi, Gao, Lifeng,  Berrang, Pascal, Livshits, Ben, and Gervais, Arthur
    In Proceedings of the ACM Web Conference 2023 2023
  4. EuroS&P
    Membership Inference Against DNA Methylation Databases
    Hagestedt, Inken, Humbert, Mathias,  Berrang, Pascal, Lehmann, Irina, Eils, Roland, Backes, Michael, and Zhang, Yang
    In Proceedings of the 2020 IEEE European Symposium on Security and Privacy (EuroS&P) 2020
  5. NDSS
    ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
    Salem, Ahmed, Zhang, Yang, Humbert, Mathias,  Berrang, Pascal, Fritz, Mario, and Backes, Michael
    In Proceedings of the 26th Annual Network and Distributed System Security Symposium (NDSS) 2019
  6. NDSSBest Paper Award
    MBeacon: Privacy-Preserving Beacons for DNA Methylation Data
    Hagestedt, Inken, Zhang, Yang, Humbert, Mathias,  Berrang, Pascal, Tang, Haixu, Wang, XiaoFeng, and Backes, Michael
    In Proceedings of the 26th Annual Network and Distributed System Security Symposium (NDSS) 2019
  7. CVCBT
    Albatross ‚Äď An optimistic consensus algorithm
    Berrang, Pascal, Styp-Rekowsky, Philipp, Wissfeld, Marvin, França, Bruno, and Trinkler, Reto
    In Proceedings of the Crypto Valley Conference on Blockchain Technology (CVCBT) 2019
  8. PoPETs
    Privacy-Preserving Similar Patient Queries for Combined Biomedical Data
    Salem, Ahmed,  Berrang, Pascal, Humbert, Mathias, and Backes, Michael
    Proceedings on Privacy Enhancing Technologies (PoPETs) 2019
  9. EuroS&P
    Dissecting Privacy Risks in Biomedical Data
    Berrang, Pascal, Humbert, Mathias, Zhang, Yang, Lehmann, Irina, Eils, Roland, and Backes, Michael
    In Proceedings of the 2018 IEEE European Symposium on Security and Privacy (EuroS&P) 2018
  10. S&P
    Identifying Personal DNA Methylation Profiles by Genotype Inference
    Backes, Michael,  Berrang, Pascal, Bieg, Matthias, Eils, Roland, Herrmann, Carl, Humbert, Mathias, and Lehmann, Irina
    In Proceedings of the 38th IEEE Symposium on Security and Privacy (S&P) 2017