Protecting Anonymity in Data-Driven Biomedical Science

  • Peter Kieseberg
  • Heidelinde Hobel
  • Sebastian Schrittwieser
  • Edgar Weippl
  • Andreas Holzinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8401)


With formidable recent improvements in data processing and information retrieval, knowledge discovery/data mining, business intelligence, content analytics and other upcoming empirical approaches have an enormous potential, particularly for the data intensive biomedical sciences. For results derived using empirical methods, the underlying data set should be made available, at least during the review process for the reviewers, to ensure the quality of the research done and to prevent fraud or errors and to enable the replication of studies. However, in particular in the medicine and the life sciences, this leads to a discrepancy, as the disclosure of research data raises considerable privacy concerns, as researchers have of course the full responsibility to protect their (volunteer) subjects, hence must adhere to respective ethical policies. One solution for this problem lies in the protection of sensitive information in medical data sets by applying appropriate anonymization. This paper provides an overview on the most important and well-researched approaches and discusses open research problems in this area, with the goal to act as a starting point for further investigation.


Anonymization pseudonymization data-driven sciences big data privacy security safety 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Peter Kieseberg
    • 1
    • 2
  • Heidelinde Hobel
    • 1
  • Sebastian Schrittwieser
    • 3
  • Edgar Weippl
    • 1
  • Andreas Holzinger
    • 2
  1. 1.Secure Business Austria ResearchAustria
  2. 2.Research Unit HCI, Institute for Medical Informatics, Statistics & DocumentationMedical University GrazAustria
  3. 3.University of Applied SciencesSt. PöltenAustria

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