A scalable software solution for anonymizing high-dimensional biomedical data

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 proc...

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Bibliographic Details
Published in:Gigascience 2021-10, Vol.10 (10)
Main Authors: Meurers, Thierry, Bild, Raffael, Do, Kieu-Mi, Prasser, Fabian
Format: Article
Language:English
Subjects:
Algorithms
Biomedical data
Data Anonymization
Data retrieval
Datasets
Genetic algorithms
Genetic transformation
Graphical user interface
Humans
Information Dissemination
Open source software
Privacy
Search algorithms
Software
Technical Note
Usability
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Summary: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. Findings For improving ARX's capability to find optimal transformations when processing high-dimensional data, we implement 2 novel search algorithms. The first is a greedy top-down approach and is oriented on a formally implemented bottom-up search. The second is based on a genetic algorithm. We evaluated the algorithms with different datasets, transformation methods, and privacy models. The novel algorithms mostly outperformed the previously implemented bottom-up search. In addition, we extended the GUI to provide a high degree of usability and performance when working with high-dimensional datasets. Conclusion With our additions we have significantly enhanced ARX's ability to handle high-dimensional data in terms of processing performance as well as usability and thus can further facilitate data sharing.
ISSN:2047-217X
2047-217X
DOI:10.1093/gigascience/giab068