Secure Business Austria Research

Vienna, Austria

Secure Business Austria Research

Vienna, Austria

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Kieseberg P.,Secure Business Austria Research | Kieseberg P.,Medical University of Graz | Hobel H.,Secure Business Austria Research | Schrittwieser S.,St. Pölten University of Applied Sciences | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

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. © Springer-Verlag Berlin Heidelberg 2014.


Kejser U.B.,Royal Library | Davidson J.,Digital Curation Center | Wang D.,Secure Business Austria Research | Strodl S.,Secure Business Austria Research | And 4 more authors.
Archiving 2014 - Final Program and Proceedings | Year: 2014

This paper presents the results of an evaluation carried out by the EU 4C project to assess how well current digital curation cost and benefit models meet a range of stakeholders needs. This work aims to elicit a means of modelling that enables comparing financial information across organisations, to support decisionmaking and for selecting the most efficient processes - all of which are critical for ensuring sustainability of digital curation investment. The evaluation revealed that the most prominent challenges are associated with the models usability, their inability to model quality and benefits of curation, and the lack of a clear terminology and conceptual description of costs and benefits. The paper provides recommendations on how these gaps in cost and benefit modelling can be bridged. © 2014 Society for Imaging Science and Technology.

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