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Title:

A scalable software solution for anonymizing high-dimensional biomedical data.

Document type:
Article; Journal Article
Author(s):
Meurers, Thierry; Bild, Raffael; Do, Kieu-Mi; Prasser, Fabian
Abstract:
BACKGROUND: Data anonymization is an important building block for ensuring privacy and fosters the reuse of data. However, transforming the data in a way that preserves the privacy of subjects while maintaining a high degree of data quality is challenging and particularly difficult when processing complex datasets that contain a high number of attributes. In this article we present how we extended the open source software ARX to improve its support for high-dimensional, biomedical datasets. FIND...     »
Journal title abbreviation:
Gigascience
Year:
2021
Journal volume:
10
Journal issue:
10
Fulltext / DOI:
doi:10.1093/gigascience/giab068
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34605868
TUM Institution:
Institut für Medizinische Statistik und Epidemiologie
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