Research at KASTEL Security Research Labs is distinguished by its broad scope. Privacy and reliability aspects are naturally part of IT security; basic and applied research complement each other, and a comprehensive approach to IT security needs to look not just at the technical system, but also at its social environment as well as its human aspects.
Our research covers a broad spectrum of cybersecurity. It combines the view of different disciplines in order to adress not only technical aspects, but also human factors and the legal framework.
Together with Wibu-Systems AG, KASTEL Security Research Labs is researching the project “Security for the Internet of Medical Things”, or “Sec4IoMT” for short. The aim is to find and exemplarily implement a solution approach for the long-term preservation of the security of Internet of Medical Things (IoMT) devices in critical infrastructures. When updating the security of individual components, the interoperability of the devices should be maintained. At the same time, a security update must not lead to a loss of certification for the medical devices.
Machine learning models require large amounts of data to be trained on, leaving little training data available in use cases with strong privacy requirements.The Secure Federated Machine Learning project of SAP and KASTEL Security Research Labs is investigating approaches for training a neural network on data from multiple parties without having to reveal their data to the other parties. These approaches can then be used in use cases with strong privacy requirements.
KASTEL Security Research Labs conduct interdisciplinary and application-oriented research. Therefore, we permanently maintain and expand numerous partnerships with industrial enterprises and research institutions. The resulting synergy effects flow into our research.
2021, the German-French cooperation PROPOLIS was launched. Prof. Thorsten Strufe from KASTEL Security Research Labs at KIT, together with SAP, the Urban Software Institute GmbH, Chemnitz, and EURECOM Frankreich, is analyzing AI based data protection for smart cities and citizens. The project will be concluded in 2024. Read more about PROPOLIS
Since August 2021, a total of 13 companies and research institutions have been working in the publicly funded “Software-Defined Car” project (SofDCar for short). The experts from industry and science want to create standardized rules and processes together for the car of the future and its effective use of data. The project is publicly funded by the German Federal Ministry of Economic Affairs and Climate Action (BMWK). Read more about SofDCar
Hilt, T.; Mack, C.; Matheis, P.; Berens, B.; Länge, T.; Haenni, R.; Koenig, R. E.; Locher, P.; Volkamer, M.
2026. 11th IEEE European Symposium on Security and Privacy (EuroS&P)
Liu, H.; Fuchß, D.; Corallo, S.; Hummel, M.; Keim, J.; Hey, T.
2026. 23rd IEEE International Conference on Software Architecture (ICSA 2026), Institute of Electrical and Electronics Engineers (IEEE)
Kirste, D.; Kannengießer, N.; Lamberty, R.; Sunyaev, A.
2026. ACM Computing Surveys, 58 (5), 1–37.
Haller, M.; Leinweber, M.; Spannagel, T.; Raiber, M.; Hartenstein, H.
2026. SysTEX’26: Proceedings of the 9th Workshop on System Software for Trusted Execution, 42–49, Association for Computing Machinery (ACM).
Hilt, T.; Mack, C.; Berens, B.; Volkamer, M.
2026. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26).
Mossano, M.; Fabian Veit, M.; Länge, T.; Maximilian Berens, B.; Sharevski, F.; Volkamer, M.
2026. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1–33, Association for Computing Machinery (ACM).