Full Citation
Title: A scalable software solution for anonymizing high-dimensional biomedical data
Citation Type: Journal Article
Publication Year: 2021
ISBN:
ISSN:
DOI: 10.1093/gigascience/giab068
NSFID:
PMCID:
PMID:
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.
Url: https://doi.org/10.1093/gigascience/giab068
User Submitted?: No
Authors: Meurers, Thierry; Bild, Raffael; Do, Kieu-Mi; Prasser, Fabian
Periodical (Full): GigaScience
Issue:
Volume: 10
Pages: 1-13
Data Collections: IPUMS Health Surveys - NHIS
Topics: Methodology and Data Collection, Population Data Science
Countries: