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Federated Information Systems

Division of Federated Information Systems

Prof. Dr. Martin Lablans

German research networks powerd by Bridgehead technology (as of 2023)

Modern oncology research happens in networks. Medicine today is becoming increasingly personalized. To get the patient numbers needed for statistical significance, collaborations spanning many establishments are necessary.

Data-driven research in the health sector depends therefore on the exploitation of distributed sources of data and tissue samples. Two significant fields of research emerge from these requirements. First, establishing the basis for carrying out research efficiently in networked federations at the technical and data content levels. Second, the implementation of regulatory requirements, which arise naturally during the processing of sensitive patient data (E.g. data protection).

In the Department of Federated Information Systems (VerbIS), we investigate problems that frequently arise in networked medical research:

  • Semantics of data, to provide a common understanding of their meaning
  • Multi-center integration of data from heterogeneous sources
  • Data protection, consent management, record linkage and pseudonymization
  • Distributed processes for evaluating and exchanging data
  • Measurement and improvement of data quality over multiple sites
We are actively engaged in developing interoperable and reusable tools for tackling the problems in this area. In addition, we have deployed our solutions in national and international treatment federations (cf. projects). With our “Bridgeheads” we have created a network of 21 exceptional partners (cf. diagram).


Prof. Dr. Martin Lablans
Federated Information Systems (E260)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 580
69120 Heidelberg
Tel: +49 6221 42 5102

Selected Publications

  • Stammler S, Kussel T, Schoppmann P, Stampe F, Tremper G, Katzenbeisser S, Hamacher K, Lablans M. Mainzelliste SecureEpiLinker (MainSEL): Privacy-Preserving Record Linkage using Secure Multi-Party Computation. Bioinformatics 2022;38:1657-68. DOI: 10.1093/bioinformatics/btaa764
  • Kussel T, Brenner T, Tremper G, Schepers J, Lablans M, Hamacher K#. Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation. J Transl Med 2022;20:458. DOI: 10.1186/s12967-022-03671-6.
  • Tremper G, Brenner T, Stampe F, Borg A, Bialke M, Croft D, Schmidt E, Lablans M. MAGICPL: A Generic Process Description Language for Distributed Pseudonymization Scenarios. Methods Inf Med 2021;60:21-31. DOI: 10.1055/s-0041-1731387
  • Juárez D, Schmidt EE, Stahl-Toyota S, Ückert F, Lablans M. A Generic Method and Implementation to Evaluate and Improve Data Quality in Distributed Research Networks. Methods Inf Med 2019;58:86-93. DOI: 10.1055/s-0039-1693685
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