Sascha Löbner is a research and teaching assistant at the Chair of Mobile Business & Multilateral Security at Goethe University Frankfurt. He holds an M.Sc. in Business Informatics and a B.Sc. in Economics and Business Administration from Goethe University. His work focuses on human-centered security and privacy in AI systems, exploring how technical mechanisms such as privacy-preserving machine learning, de-identification, and explainable AI can align with user expectations and regulatory demands.
His research combines empirical, user-centered methods (interviews, surveys, mixed-methods studies) with technical evaluations to improve trust, transparency, and usability in domains like smart homes, generative AI, resume screening, and vehicular data sharing. He contributes to international conferences such as PETs, HICSS, ARES, PST, PACIS, and IFIP, advancing interdisciplinary insights at the intersection of usability, privacy, and AI security.
- Mitigating Bias in Recruitment: A Practical Approach to CV De-identification Considering Privacy Sensitive Information
Löbner, S., Serna, J., Tronnier, F., Tesfay, W., Rannenberg, K.: In Availability, Reliability and Security. ARES 2025. Lecture Notes in Computer Science, vol 15995. Springer, Cham (2025).
Download PDF - Regulatory Challenges in Cybersecurity – A Critical Analysis of the EU AI Act
Tronnier, F., Löbner, S., Lacombe, M.H., Rannenberg, K.: In Information Security Education. Empowering People through Information Security Education. Springer Nature Switzerland (May 2025).
Download PDF - De-identification of privacy sensitive information in resumes with GPT-4: A utility analysis for automated job role classification
Löbner, S., Tronnier, F., Linke, D. (2025). In Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS).
https://doi.org/10.24251/hicss.2025.017
Download PDF - Consulting in the Age of AI: A Qualitative Study on the Impact of Generative AI on Management Consultancy Services
Tronnier, F., Bernet, R., Löbner, S., & Rannenberg, K. (2025). Consulting in the age of AI: A qualitative study on the impact of generative AI on management consultancy services. In Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS).
https://doi.org/10.24251/hicss.2025.017
Download PDF - User Issues and Concerns in Generative AI: A Mixed-Methods Analysis of App Reviews
Bracamonte, V., Löbner, S., Tronnier, F., Lieberknecht, A. K., & Pape, S. (2024). User issues and concerns in generative AI: A mixed-methods analysis of app reviews. In International Conference on Computer-Human Interaction Research and Applications (pp. 304–322). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-82633-7_19
Download PDF - Which PPML Would a User Choose? A Structured Decision Support Framework for Developers to Rank PPML Techniques Based on User Acceptance Criteria
Löbner, S., Pape, S., Bracamonte, V., & Phalakarn, K. (2024). Which PPML would a user choose? A structured decision support framework for developers to rank PPML techniques based on user acceptance criteria. arXiv preprint arXiv:2411.06995.
Download PDF - An In-Depth Analysis of Security and Privacy Concerns in Smart Home IoT Devices Through Expert User Interviews
Löbner, S., Tronnier, F., Miller, L., & Lindemann, J. (2024). An in-depth analysis of security and privacy concerns in smart home IoT devices through expert user interviews. In IFIP World Conference on Information Security Education (pp. 97–110). Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-62918-1_7
Download PDF - Better Than Ever? Analyzing The Impact of Change in Consensus Mechanism On Market Liquidity For Ethereum.
Tronnier, F., Stoev, A., Hamm, P., & Löbner, S. (2024). Better than ever? Analyzing the impact of change in consensus mechanism on market liquidity for Ethereum. In PACIS 2024 Proceedings.
https://aisel.aisnet.org/pacis2024/track02_blockchain/track02_blockchain/1
Download PDF - A Systematic Literature Review on Gender Bias in AI–Towards Inclusiveness in Machine Learning
Tronnier, F., Löbner, S., Azanbayev, A., & Walter, M. L. (2024). A systematic literature review on gender bias in AI–Towards inclusiveness in machine learning. In PACIS 2024 Proceedings.
https://aisel.aisnet.org/pacis2024/track01_aibussoc/track01_aibussoc/3
Download PDF - Systematizing the State of Knowledge in Detecting Privacy Sensitive Information in unstructured texts using machine learning
Löbner, S., Tesfay, W. B., Bracamonte, V., & Nakamura, T. (2023). Systematizing the state of knowledge in detecting privacy sensitive information in unstructured texts using machine learning. In 20th Annual International Conference on Privacy, Security and Trust (PST).
https://doi.org/10.1109/pst58708.2023.10320187
Download PDF - User Acceptance Criteria for Privacy Preserving Machine Learning Techniques
Löbner, S., Pape, S., & Bracamonte, V. (2023). User acceptance criteria for privacy preserving machine learning techniques. In Proceedings of the 18th International Conference on Availability, Reliability and Security (ARES). ACM.
https://doi.org/10.1145/3600160.3605004
Download PDF - An Evaluation of Information Flows in Digital Euro Transactions Using Contextual Integrity Theory
Tronnier, F., Biker, P., Baur, E., & Löbner, S. (2023). An evaluation of information flows in digital euro transactions using contextual integrity theory. In Proceedings of the 2023 European Interdisciplinary Cybersecurity Conference (EICC).
https://doi.org/10.1145/3590777.3590779
Download PDF - Privacy Preserving Data Analysis with the Encode, Shuffle, Analyse Architecture in Vehicular Data Sharing
Löbner, S., Gartner, C., & Tronnier, F. (2023). Privacy preserving data analysis with the Encode, Shuffle, Analyse architecture in vehicular data sharing. In Proceedings of the 2023 European Interdisciplinary Cybersecurity Conference (EICC).
https://doi.org/10.1145/3590777.3590791
Download PDF - Comparing the Effect of Privacy and Non-Privacy Social Media Photo Tools on Factors of Privacy Concern
Bracamonte, V., Pape, S., & Löbner, S. (2023). Comparing the effect of privacy and non-privacy social media photo tools on factors of privacy concern. In Proceedings of the 9th International Conference on Information Systems Security and Privacy (ICISSP), 669–676.
https://doi.org/10.5220/0011784900003405
Download PDF - Effectiveness and Information Quality Perception of an AI Model Card
Bracamonte, V., Pape, S., Löbner, S., & Tronnier, F. (2023). Effectiveness and information quality perception of an AI model card: A study among non-experts. In 20th Annual International Conference on Privacy, Security and Trust (PST).
https://doi.org/10.1109/pst58708.2023.10320197
Download PDF - Study on the Technical Evaluation of De-Identification Procedures for Personal Data in the Automotive Sector
Rannenberg, K., Pape, S., Tronnier, F., & Löbner, S. (2023). Study on the technical evaluation of de-identification procedures for personal data in the automotive sector. Technical report, Goethe University Frankfurt.
https://doi.org/10.21248/gups.63413
Download PDF - “All Apps Do This”: Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content
Bracamonte, V., Pape, S., & Löbner, 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, 2022(4), 45–63.
https://doi.org/10.56553/popets-2022-0062
Download PDF - Enhancing Privacy in Federated Learning with Local Differential Privacy for Email Classification
Löbner, S., Gogov, B., & Tesfay, W. B. (2022). Enhancing privacy in federated learning with local differential privacy for email classification. In International Workshop on Data Privacy Management (DPM), 3–18.
https://doi.org/10.1007/978-3-031-25734-6_1
Download PDF - A Discussion on Ethical Cybersecurity Issues in Digital Service Chains
Tronnier, F., Pape, S., Löbner, S., & Rannenberg, K. (2022). A discussion on ethical cybersecurity issues in digital service chains. In Cybersecurity of Digital Service Chains – Challenges, Methodologies, and Tools (pp. 222–256). Springer, Lecture Notes in Computer Science (LNCS), vol. 13300.
https://doi.org/10.1007/978-3-031-04036-8_10
Download PDF - Explainable Machine Learning for Privacy Setting Prediction
Löbner, S., Tesfay, W. B., Nakamura, T., & Pape, S. (2021). Explainable machine learning for default privacy setting prediction. IEEE Access, 9, 63700–63717.
https://doi.org/10.1109/ACCESS.2021.3074676
Download PDF - Comparison of De-Identification Techniques for Privacy Preserving Data Analysis in Vehicular Data Sharing
Löbner, S., Tronnier, F., Pape, S., & Rannenberg, K. (2021). Comparison of de-identification techniques for privacy preserving data analysis in vehicular data sharing. In Proceedings of the 5th ACM Computer Science in Cars Symposium (CSCS), 1–11.
https://doi.org/10.1145/3488904.3493380
Download PDF