An Open Source Python Library for Anonymizing Sensitive Data

Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to meet in compliance with strict data protection regul...

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Bibliographic Details
Published in:Scientific data Vol. 11; no. 1; pp. 1289 - 14
Main Authors: Sáinz-Pardo Díaz, Judith, López García, Álvaro
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 26.11.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/1046
639/705/117
639/705/794
Humanities and Social Sciences
Libraries
multidisciplinary
Open data
Science
Science (multidisciplinary)
Sensitivity analysis
ISSN:2052-4463, 2052-4463
Online Access:Get full text
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Summary:Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to meet in compliance with strict data protection regulations. Consequently, researchers need to rely on proven methods that allow them to anonymize their data without sharing it with third parties. To this end, this paper presents the implementation of a Python library for the anonymization of sensitive tabular data. This framework provides users with a wide range of anonymization methods that can be applied on the given dataset, including the set of identifiers, quasi-identifiers, generalization hierarchies and allowed level of suppression, along with the sensitive attribute and the level of anonymity required. The library has been implemented following best practices for integration and continuous development, as well as the use of workflows to test code coverage based on unit and functional tests.
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ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-04019-z