Dr. Fabian Prasser

Thu, October 06 2016

fabian.prasser@tum.de

Ph.D. in Computer Science / Biomedical Informatics (Dr. rer. nat.), TUM
Degree in Computer Science (Dipl.-Inform.), TUM

Focus of Work:

Information Systems for Research, Knowledge and Information Management, Information Integration, Database Systems, Data Privacy, Data Anonymization and De-Identification

01/2013 - Postdoctoral Researcher, Chair of Medical Informatics, Institute of Medical Statistics and Epidemiology, TUM
08/2012 – 10/2012 Research Assistant, Chair for Database Systems, Department of Computer Science, TUM
01/2009 – 07/2012 Doctoral Studies, Scholarship Holder, Graduate School of Information Science in Health, TUM

GI Gesellschaft für Informatik e.V. (German Assoc. Computer Science), Member
GMDS German Association for Medical Informatics, Biometry, and Epidemiology, Member

Selected Publications

  1. Prasser F, Kohlbacher O, Mansmann U, Bauer B, Kuhn KA. Data Integration for Future Medicine (DIFUTURE). Methods of Information in Medicine. 2018 Jul;57(S 01):e57-65. doi: 10.3414/ME17-02-0022
  2. Bild R, Kuhn KA, Prasser F. SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees. Proceedings on Privacy Enhancing Technologies. 2018 Jan 1;2018(1):67-87. doi: 10.1515/popets-2018-0004
  3. Prasser F, Gaupp J, Wan Z, Xia W, Vorobeychik Y, Kantarcioglu M, Kuhn KA, Malin B. An Open Source Tool for Game Theoretic Health Data De-Identification. AMIA Annual Symposium Proceedings 2017 (Vol. 2017, p.1430). American Medical Informatics Association.
  4. Prasser F, Eicher J, Bild R, Spengler H, Kuhn KA. A Tool for Optimizing De-Identified Health Data for Use in Statistical Classification.  2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), 169-174. doi: 10.1109/CBMS.2017.105
  5. Eicher J, Kuhn KA, Prasser F. An Experimental Comparison of Quality Models for Health Data De-Identification. Studies in Health Technology and Informatics 2017;245:704-8. doi: 10.3233/978-1-61499-830-3-704
  6. Prasser F, Kohlmayer F, Spengler H, Kuhn KA. A scalable and pragmatic method for the safe sharing of high-quality health data. IEEE Journal of Biomedical and Health Informatics 2018 Mar;22(2):611-622. doi: 10.1109/JBHI.2017.2676880.
    F Prasser received the Johann Peter Süßmilch-Medal of the German Association of Medical Informatics, Biometry and Epidemiology (GMDS) for this work.
  7. Prasser F, Bild R, Kuhn KA. A generic method for assessing the quality of de-identified health data. Proc MIE 2016 at HEC 2016. August 2016. IOS Press. doi: 10.3233/978-1-61499-678-1-312.
    F Prasser received the Peter L Reichertz Prize of the European Federation of Medical Informatics for this work.
  8. Prasser F, Bild R, Eicher J, Spengler H, Kohlmayer F, Kuhn KA. Lightning: Utility-Driven Anonymization of High-Dimensional Data. Trans Data Privacy. 2016 Aug; 9:2 (2016) 161 – 185.
  9. Prasser F*, Kohlmayer F*, Kuhn KA. The importance of context: risk-based de-identification of biomedical data. Methods Inf Med. 2016 Aug 5;55(4):347-55. doi: 10.3414/ME16-01-0012.
  10. Prasser F, Kohlmayer F, Kuhn KA. Efficient and effective pruning strategies for health data de-identification. BMC Med Inform Decis Mak. 2016 Apr 30;16:49. doi: 10.1186/s12911-016-0287-2.
  11. F. Prasser, M. Sariyar, Anonymisierung medizinischer Individualdaten. In: J. Drepper, S. Semler (Eds.): IT-Infrastrukturen in der patientenorientierten Forschung. Aktueller Stand und Handlungsbedarf. Verfasst und vorgelegt vom IT-Reviewing-Board der TMF. 2016; AKA.
  12. Lautenschläger R, Kohlmayer F, Prasser F, Kuhn KA. A generic solution for web-based management of pseudonymized data. BMC Med Inform Decis Mak. 2015 Nov 30;15(1):100. doi: 10.1186/s12911-015-0222-y.
  13. Kohlmayer F*, Prasser F*, Kuhn KA. The cost of quality: Implementing generalization and suppression for anonymizing biomedical data with minimal information loss. J Biomed Inform. 2015 Sep 15. doi: 10.1016/j.jbi.2015.09.007.
  14. Prasser F*, Kohlmayer F*, Lautenschläger R, Kuhn KA. ARX - A comprehensive tool for anonymizing biomedical data. AMIA Annu Symp Proc. 2014 Nov 14;2014:984-93.
  15. Prasser F*, Kohlmayer F*. Putting statistical disclosure control into practice: The ARX data anonymization tool. In: Gkoulalas-Divanis, Aris, Loukides, Grigorios (Eds.): Medical Data Privacy Handbook, 2015 Nov; Springer. doi: 10.1007/978-3-319-23633-9_6 .
  16. Kohlmayer F*, Prasser F*, Eckert C, Kuhn KA. A flexible approach to distributed data anonymization. J Biomed Inform. 2014 Aug;50:62-76. doi: 10.1016/j.jbi.2013.12.002.
  17. Prasser F*, Kohlmayer F*, Kuhn KA. A benchmark of globally-optimal anonymization methods for biomedical data. Proc Int Symp Computer-Based Med Syst, 2014 May; 66-71. IEEE. 
  18. Prasser F, Kemper A, Kuhn KA. Efficient distributed query processing for autonomous RDF databases. Proc Int Conf Extending Database Technology. 2012 Aug; 372-383. ACM. doi: 10.1145/2247596.2247640 .
  19. Kohlmayer F*, Prasser F*, Eckert C, Kemper A, Kuhn KA. Flash: efficient, stable and optimal k-anonymity. Proc Int Conf Information Privacy, Security, Risk Trust, 2012 Sep. IEEE. doi: 10.1109/SocialCom-PASSAT.2012.52 .
  20. Kohlmayer F*, Prasser F*, Eckert C, Kemper A, Kuhn KA. Highly efficient optimal k-anonymity for biomedical datasets. Proc Int Symp Computer-Based Med Syst, 2012 Jun; 708-717. IEEE. doi: 10.1109/CBMS.2012.6266366
  21. Prasser F, Kohlmayer F, Kemper A, Kuhn KA. A generic transformation of HL7 messages into the Resource Description Framework data model. Lecture Notes in Informatics (GI Edition). 2012; P-208:1559-64.

(*) F Prasser and F Kohlmayer contributed equally to this work.