ArticleGigaScience2021
A scalable software solution for anonymizing high-dimensional biomedical data.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Open tools for quantitative anonymization of tabular phenotype data: literature review.Briefings in bioinformatics · 2022Pooled it
- Parallel privacy preservation through partitioning (P4): a scalable data anonymization algorithm for health data.BMC medical informatics and decision making · 2025Article
- Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.BMC medical informatics and decision making · 2024Article
- Ten Topics to Get Started in Medical Informatics Research.Journal of medical Internet research · 2023Article
- Advances in human intracranial electroencephalography research, guidelines and good practices.NeuroImage · 2022Review
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData 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.
findingsFor 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.
conclusionWith 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.