Chair of Mobile Business & Multilateral Security

Sascha Löbner, M.Sc.

Research Assistant

 Foto sascha
Phone&Fax:
+49 (0) 69 / 34706 (Phone)
E-mail & Home Page:
Address:
Theodor-W.-Adorno-Platz 4
Office 2.236, RuW Building
D-60323 Frankfurt am Main 

 

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 Generative AI.

Publications:
  • Tronnier, F., Löbner, S., Azanbayev, A.,; and Walter, M.L., "A Systematic Literature Review on Gender Bias in AI – Towards Inclusiveness in Machine Learning" (2024). PACIS 2024 Proceedings. 3. Available under: https://aisel.aisnet.org/pacis2024/track01_aibussoc/track01_aibussoc/3
  • Tronnier, F., Stoev, A., Hamm, P., and Löbner, S.,, "Better Than Ever? Analyzing The Impact of Change in Consensus Mechanism On Market Liquidity For Ethereum" (2024). PACIS 2024 Proceedings. 1. Available under: https://aisel.aisnet.org/pacis2024/track02_blockchain/track02_blockchain/1
  • Sascha Löbner, Frédéric Tronnier, László Miller and Jens Lindemann (2024), An In-Depth Analysis of Security and Privacy Concerns in Smart Home IoT Devices Through Expert User Interviews, World Conference on Information Security Education
  • Sascha Löbner, Sebastian Pape, and Vanessa Bracamonte. 2023. User Acceptance Criteria for Privacy Preserving Machine Learning Techniques. In Proceedings of the 18th International Conference on Availability, Reliability and Security (ARES '23). Association for Computing Machinery, New York, NY, USA, Article 149, 1–8. https://doi.org/10.1145/3600160.3605004
  • Sascha Löbner, Christian Gartner, and Frédéric Tronnier. 2023. Privacy Preserving Data Analysis with the Encode, Shuffle, Analyse Architecture in Vehicular Data Sharing. In European Interdisciplinary Cybersecurity Conference (EICC2023). Association for Computing Machinery, New York, NY, USA, 85–91. https://doi.org/10.1145/3590777.3590791
  • Bracamonte, V., Pape, S., Löbner, S., & Tronnier, F. (2023, August). Effectiveness and Information Quality Perception of an AI Model Card: A Study Among Non-Experts. In 2023 20th Annual International Conference on Privacy, Security and Trust (PST) (pp. 1-7). IEEE.
  • Bracamonte, V., Pape, S., & Löbner, S. (2023, June). Factors of Intention to Use a Photo Tool: Comparison Between Privacy-Enhancing and Non-privacy-enhancing Tools. In IFIP International Conference on ICT Systems Security and Privacy Protection (pp. 321-334). Cham: Springer Nature Switzerland.
  • 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
  • Frédéric Tronnier, Patrick Biker, Erik Baur, and Sascha Löbner. 2023. An Evaluation of Information Flows in Digital Euro Transactions Using Contextual Integrity Theory. In European Interdisciplinary Cybersecurity Conference (EICC2023). Association for Computing Machinery, New York, NY, USA, 7–12. https://doi.org/10.1145/3590777.3590779
  • Löbner, S., Tesfay, W. B., Bracamonte, V., & Nakamura, T. (2023, August). Systematizing the State of Knowledge in Detecting Privacy Sensitive Information in Unstructured Texts using Machine Learning. In 2023 20th Annual International Conference on Privacy, Security and Trust (PST) (pp. 1-7). IEEE.
  • Bracamonte, V., Pape, S., & Loebner, S. (2023). Comparing the Effect of Privacy and Non-Privacy Social Media Photo Tools on Factors of Privacy Concern. In ICISSP (pp. 669-676).
  • 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
  • Bracamonte, V., Pape, S., & Loebner, S. (2022). “All apps do this”: Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content. Proceedings on Privacy Enhancing Technologies, 3, 57-78.
  • Löbner, S., Tesfay, W. B., Nakamura, T., & Pape, S. (2021). Explainable machine learning for default privacy setting prediction. IEEE Access, 9, 63700-63717, doi: 10.1109/ACCESS.2021.3074676.