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

ABSTRACT: 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. 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 wi

SUBMITTER: Meurers T 

PROVIDER: S-EPMC8489190 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature