Medical Data (Privacy)

COMSYS is invested in interdisciplinary research activities that tackle life sciences for several years. In this context, our research activities are not limited to a specific theme. Instead, we look at a wide range of research challenges (with our applied cooperation partners). In particular, we have prior experience with the following topics:

  • Interoperability: FAIRifying medical (research) data and ensuring interoperability between different processing steps are timely challenges. Addressing them promises to advance Big Data applications and boost privacy research.
  • Consent: For healthcare research, informed consent is a key principle. Unfortunately, obtaining consent is a challenging endeavor given the lack of usable and accepted solutions in the area. Thus, novel solutions are needed.
  • Confidentiality: Privacy is a critical aspect when handling (sensitive) patient data. Exploiting data globally and handling different data types calls for future research activities. Specifically, we apply well-known concepts from information security to provide confidentiality guarantees for involved stakeholders.
  • Representation: Given the diversity of medical research data (e.g., *-omics) and data formats (tabular data, image data, etc.), finding, linking, and joining (sparse) data is not a trivial task. Accordingly, COMSYS is looking into promising approaches and new directions in the area.
  • Distributed Analytics: Concepts like federated learning are particularly important for medical contexts, where data might be scattered across institutions. Hence, we look into these concepts to identify attack vectors, close privacy leaks, and showcase new application areas.
  • Data Quality: New findings depend on a solid foundation. In the life sciences setting, data quality and corresponding guarantees are essential (particularly in distributed settings). Consequently, we also research technical building blocks that promise to improve data quality (in distributed settings).

A Selection of Projects and Teaching Activities

Projects:

  • COAT: Computational ecosystem for clinical applications of organ crosstalk (ERS PFExC005, 2022-2024)
  • myneData: Self-determined Utilization of Personal Data with Inherent Protection of Privacy and Data (BMBF, 2016-2019)

Teaching Activities:

Publications

6.
Proceedings of the 1st Conference on Building a Secure and Empowered Cyberspace (BuildSEC '24), December 19-21, 2024, New Delhi, India
Publisher: IEEE,
December 2024
Accepted
5.
Johannes Lohmöller, Jannis Scheiber, Rafael Kramann, Klaus Wehrle, Sikander Hayat, and Jan Pennekamp
scE(match): Privacy-Preserving Cluster Matching of Single-Cell Data
Proceedings of the International Workshop on AI-Driven Trust, Security and Privacy in Computer Networks (AI-Driven TSP '24), co-located with the 23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom '24), December 17-21, 2024, Sanya, China
Publisher: IEEE,
December 2024
Accepted
4.
Benedikt von Querfurth, Johannes Lohmöller, Jan Pennekamp, Tore Bleckwehl, Rafael Kramann, Klaus Wehrle, and Sikander Hayat
mcBERT: Patient-Level Single-cell Transcriptomics Data Representation
bioRxiv,
November 2024
3.
Research Papers of the Platform Privacy, 2024, October 17-18, Berlin, Germany Volume 4, page 7-12.
Publisher: Fraunhofer ISI,
October 2024
2.
Johannes Lohmöller, Jan Pennekamp, Roman Matzutt, Carolin Victoria Schneider, Eduard Vlad, Christian Trautwein, and Klaus Wehrle
Data & Knowledge Engineering, 151
May 2024
ISSN: 0169-023X
1. BMC Medical Genomics, 10(Suppl 2):29-42
July 2017
ISSN: 1755-8794
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