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70569 Stuttgart
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Room: 2.328
Office Hours
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Subject
As data-driven technologies become increasingly central in modern industry, the ability to work with high-quality data while safeguarding individual privacy has become a fundamental challenge. Organisations aim to derive valuable insights from their data, yet must also ensure that sensitive information remains protected. Anonymization plays a crucial role in this balance: it enables data to be collected and analysed responsibly, provided that suitable techniques are selected and correctly applied.
However, the wide variety of available anonymization methods makes it difficult for developers to understand their guarantees, assess their effectiveness, and identify the right approach for a specific use case. This uncertainty is further heightened by evolving re-identification attacks and the growing complexity of industrial data, for example in the automotive domain.
The aim of my research is therefore to investigate how anonymization can be designed, evaluated and applied in a systematic and practical way. I focus on the empirical comparison of anonymization techniques, decision-support mechanisms for choosing appropriate methods, and the use of attack models to measure privacy protection. In the long term, my work seeks to contribute to a paradigm that enables developers to integrate anonymization by design and to use it effectively in real-world data-driven systems.
Citations
- Google Scholar: Profile
- ORCID: 0009-0007-9126-6021
2025
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Andrea Fieschi, Christoph Stach, Pascal Hirmer, and Bernhard Mitschang. 2025. Navigating the Anonymization Landscape: An Ontological Support for Developers to Make Informed Privacy Decisions. In Information Systems Security and Privacy. ICISSP 2025. Springer. To appear.
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Andrea Fieschi, Pascal Hirmer, and Christoph Stach. Discovering Suitable Anonymization Techniques: A Privacy Toolbox for Data Experts. In Proceedings of the 21st Conference on Database Systems for Business, Technology and Web (BTW ’25) (Bamberg, March 2025). Edited by Meike Klettke, Ralf Schenkel, Andreas Heinrich, Daniela Nicklas, Maximilian E. Schüle, and Klaus Meyer-Wegener. Volume P361 of LNI, GI, pages 827–833. issn: 2944-7682.
[PDF] [Citation] [DOI] -
Andrea Fieschi, Pascal Hirmer, Christoph Stach, and Bernhard Mitschang. Characterising and Categorising Anonymization Techniques: A Literature-Based Approach. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1 (ICISSP ’25) (Porto, February 2025). Edited by Roberto Di Pietro, Karen Renaud, and Paolo Mori. ICISSP ’25, SciTePress, pages 107–118. isbn: 978-989-758-735-1.
[PDF] [Citation] [DOI] - Arber Shoshi, Yuchen Xia, Andrea Fieschi, Yannick Baumgarten, Andrea Gaißler, Thomas Ackermann, Peter Reimann, Michael Weyrich, Bernhard Mitschang, Thomas Bauernhansl, and Robert Miehe. An Analysis of Monitoring Solutions for CAR T Cell Production. Healthcare Technology Letters. 2025 Jan;12(1):e70012.
[PDF] [Citation] [DOI]
2024
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Andrea Fieschi, Pascal Hirmer, Sachin Agrawal, Christoph Stach, and Bernhard Mitschang. HySAAD – A Hybrid Selection Approach for Anonymization by Design in the Automotive Domain. In Proceedings of the 25th IEEE International Conference on Mobile Data Management (Brussels, June 2024). Edited by Chiara Renso, Mahmoud Sakr, Walid G Aref, Ashley Song, and Cheng Long. MDM ’24, IEEE, pages 203–210. isbn: 979-8-3503-7455-1.
[PDF] [Citation] [DOI] - Arber Shoshi, Yuchen Xia, Andrea Fieschi, Thomas Ackermann, Peter Reimann, Michael Weyrich, Bernhard Mitschang, Thomas Bauernhansl, and Robert Miehe.. A flexible digital twin framework for ATMP production–Towards an efficient CAR T Cell Manufacturing. Procedia CIRP. 2024 Jan 1;125:124-9.
[PDF] [Citation] [DOI]
2023
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Andrea Fieschi, Yunxuan Li, Pascal Hirmer, Christoph Stach, and Bernhard Mitschang. Privacy in Connected Vehicles: Perspectives of Drivers and Car Manufacturers. In Proceedings of the 17th Symposium and Summer School on Service-Oriented Computing (SummerSOC 2023) (Heraklion, June 2023). Edited by Marco Aiello, Johanna Barzen, Schahram Dustdar, and Frank Leymann. Volume 1847 of CCIS, Springer, pages 59–68. isbn: 978-3-031-45727-2.
[PDF] [Citation] [DOI] - Andrea Fieschi, Pascal Hirmer, Rose Sturm, Martin Eisele, and Bernhard Mitschang. Anonymization use cases for data transfer in the automotive domain. In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), pp. 98-103. IEEE, 2023.
[PDF] [Citation] [DOI]
In Progress
- [Master Thesis] Systematic Evaluation of Retrieval-Augmented Generation Technology Stacks for Recommender Systems
- [Master Thesis] Retrievel-Augmented Recommender System for Data
Anonymization Methods - [Research Project] Implementation of Re-identification Attacks for Evaluating Anonymization Techniques
Completed
- [Bachelor Thesis] Re-Identification Attacks to Validate the Privacy Provided by Anonymization
- [Research Project] Recommending Appropriate Anonymization Techniques with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG)
- [Master Thesis] Enhancing Privacy in Car Data: Anonymization Techniques and Metrics Evaluation
- [Master Thesis] Anonymisierung von Daten: Von der Literatur zum Automobilbereich