• Formal Foundations of Computer Science
• Kryptographie I
• Cryptography
• Cryptography II
• Algorithmics
• Data Security
• Bachelor/Master Seminar

Computation on encrypted or masked values – Privacy preserving machine learning 

• Multi-Party Computation
• Homomorphic Encryption
• Functional Encryption
• Differential Privacy
• Anonymization

• Linda Scheu-Hachtel and Jasmin Zalonis.  “Fully Encrypted Machine Learning Training Using Function-Hiding Functional Encryption.” International Conference on Applied Cryptography and Network Security. 2026.
• Jasmin Zalonis, Linda Scheu-Hachtel, and Frederik Armknecht. “A New Construction Method for More Efficient Quadratic One-Time Noisy Multi-Client Functional Encryption Schemes.” Proceedings of the ACM Asia Conference on Computer and Communications Security. 2026.
• Linda Scheu-Hachtel and Jasmin Zalonis. “Enhancing Noisy Functional Encryption for Privacy-Preserving Machine Learning.” IEEE Annual Computer Security Applications Conference (ACSAC). 2025.
• Jasmin Zalonis, Linda Scheu-Hachtel, and Frederik Armknecht. “A New Quadratic Noisy Functional Encryption Scheme and Its Application for Privacy Preserving Machine Learning.” International Conference on Applied Cryptography and Network Security. 2025.
• Jasmin Zalonis, Frederik Armknecht, and  Linda Scheu-Hachtel. “Differentially Private Functional Encryption.” Proceedings on Privacy Enhancing Technologies. 2024.
• Jasmin Zalonis, Frederik Armknecht, Björn Grohmann, Manuel Koch. “Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context.”  arXiv. 2022.