About the Scientist

Felix Morsbach is a PhD student at the chair of Privacy and Security at KIT. He has a background in software engineering, embedded systems and high performance computing. His current research interest lies in privacy-preserving data analytics with a strong focus on privacy-preserving machine learning with differential privacy guarantees.

Research Interests

Privacy-Preserving Machine Learning
Membership Inference Attacks
Technical Privacy Notions (e.g. Differential Privacy)
Privacy Enhancing Technologies
Federated and Collaborative Learning

If you are a student at KIT and interested in writing your Bachelor or Master thesis in one of the topics above, please feel encouraged to contact me. I am always looking for motivated students.

Curriculum Vitæ

08/2022 – today: Research associate at the Institute of Information Security and Dependability (KASTEL), Karlsruhe Institute of Technology, Karlsruhe, Germany
10/2020 – 07/2022: Research associate at the Institute of Applied Informatics and Formal Description Methods, Karlsruhe Institute of Technology, Karlsruhe, Germany
11/2019 – 01/2020: Teaching Assistant in Constraint Programming for Combinatorial Optimisation at Uppsala Universitet, Sweden
10/2018 – 07/2020: Master of Science in Computer Science at Uppsala Universitet, Sweden
10/2017 – 07/2018: Software Engineer for Embedded Systems at SIEMENS AG in Karlsruhe, Germany
10/2014 – 09/2017: Bachelor of Science in Applied Computer Science at Duale Hochschule Baden-Württemberg, Karlsruhe, Germany
09/2014 – 09/2017: Cooperative Student at SIEMENS AG in Karlsruhe, Germany

Reviewer for

ACM ASIA Conference on Computer and Communication Security (ASIACCS)
European Symposium on Research in Computer Security (ESORICS)
IEEE Transactions on Information Forensics and Security (IEEE T-IFS)

Publications

Human-Centered Design for Data-Sparse Tailored Privacy Information Provision
Goram, M.; Dehling, T.; Morsbach, F.; Sunyaev, A.
2023. Human Factors in Privacy Research. Ed.: N. Gerber, 283–298, Springer International Publishing. doi:10.1007/978-3-031-28643-8_14

Why Do Machine Learning Practitioners Still Use Manual Tuning? A Qualitative Study
Hasebrook, N.; Morsbach, F.; Kannengießer, N.; Zöller, M.; Franke, J.; Lindauer, M.; Hutter, F.; Sunyaev, A.
2022. Karlsruher Institut für Technologie (KIT). doi:10.48550/ARXIV.2203.01717

Architecture Matters: Investigating the Influence of Differential Privacy on Neural Network Design
Morsbach, F.; Dehling, T.; Sunyaev, A.
2021. Presented at NeurIPS 2021 Workshop on Privacy in Machine Learning (PriML 2021), 14.12.2021. doi:10.48550/arXiv.2111.14924

DecFL: An Ubiquitous Decentralized Model Training Protocol and Framework Empowered by Blockchain
Morsbach, F.; Toor, S.
2021. BSCI ’21: Proceedings of the 3rd ACM International Symposium on Blockchain and Secure Critical Infrastructure, 61–68, Association for Computing Machinery (ACM). doi:10.1145/3457337.3457842