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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| Vydané v: | Scientific data Ročník 11; číslo 1; s. 1289 - 14 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Nature Publishing Group UK
26.11.2024
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2052-4463, 2052-4463 |
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| Abstract | 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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| AbstractList | 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. 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.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. Abstract 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. |
| ArticleNumber | 1289 |
| Author | Sáinz-Pardo Díaz, Judith López García, Álvaro |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39592621$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/978-3-319-45381-1_5 10.1145/1150402.1150499 10.1038/s41586-020-2649-2 10.1145/3631326 10.1109/CSR57506.2023.10224917 10.1142/S0218488502001648 10.14778/2350229.2350255 10.1109/ICDE.2007.367856 10.1145/1217299.1217302 10.1038/s41597-022-01894-2 10.1007/978-3-319-23633-9_6 10.1145/3457607 10.1145/1401890.1401904 10.5281/zenodo.13320086 10.25080/Majora-92bf1922-00a 10.24432/C5XW20 10.1504/IJBIDM.2022.123216 |
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| References | 4019_CR15 4019_CR16 4019_CR13 4019_CR14 4019_CR11 4019_CR12 4019_CR10 V Ayala-Rivera (4019_CR19) 2014; 7 J Sáinz-Pardo Daz (4019_CR23) 2022; 9 4019_CR1 4019_CR7 4019_CR6 4019_CR9 N Yuvaraj (4019_CR20) 2022; 20 4019_CR3 4019_CR2 4019_CR5 4019_CR17 4019_CR4 4019_CR18 4019_CR26 4019_CR24 4019_CR25 4019_CR22 4019_CR21 CR Harris (4019_CR27) 2020; 585 L Sweeney (4019_CR8) 2002; 10 4019_CR28 39706836 - Sci Data. 2024 Dec 20;11(1):1414. doi: 10.1038/s41597-024-04283-z |
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| Title | An Open Source Python Library for Anonymizing Sensitive Data |
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