Sascha Löbner is a research and teaching assistant at the Chair of Mobile Business & Multilateral Security. He holds a M.Sc. in Business Informatics and a B.Sc. in Economics and Business Administration, both from Goethe University Frankfurt. During his Master degree he specialized on Machine Learning, Distributed Systems and High Performance Computer Applications. His Master thesis on “Explainable Machine Learning for Default Privacy Setting Prediction” has been the key driver that led him to join the m-chair.

Currently, he is working in the field of Privacy Preserving Machine Learning and especially Federated Learning. 

Publications:

• S. Löbner, W. B. Tesfay, T. Nakamura and S. Pape, "Explainable Machine Learning for Default Privacy Setting Prediction," in IEEE Access, doi: 10.1109/ACCESS.2021.3074676.
• Sascha Löbner, Frédéric Tronnier, Sebastian Pape, and Kai Rannenberg. 2021. Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing. In Computer Science in Cars Symposium (CSCS '21). Association for Computing Machinery, New York, NY, USA, Article 7, 1–11. DOI: https://doi.org/10.1145/3488904.3493380
• Tronnier, F., Pape, S., Löbner, S., Rannenberg, K. (2022). A Discussion on Ethical Cybersecurity Issues in Digital Service Chains. In: Kołodziej, J., Repetto, M., Duzha, A. (eds) Cybersecurity of Digital Service Chains. Lecture Notes in Computer Science, vol 13300. Springer, Cham. https://doi.org/10.1007/978-3-031-04036-8_10
• "All apps do this": Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content; Vanessa Bracamonte (KDDI Research, Inc), Sebastian Pape (Goethe University Frankfurt), Sascha Loebner (Goethe University Frankfurt)

• Löbner, S., Gogov, B., Tesfay, W.B. (2023). Enhancing Privacy in Federated Learning with Local Differential Privacy for Email Classification. In: Garcia-Alfaro, J., Navarro-Arribas, G., Dragoni, N. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2022 2022. Lecture Notes in Computer Science, vol 13619. Springer, Cham. https://doi.org/10.1007/978-3-031-25734-6_1