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Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19
The Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) is a European registry for studying the epidemiology and clinical course of COVID-19. To support evidence-generation at the rapid pace required in a pandemic, LEOSS follows an Open Science approach, making data available to the pu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729909/ https://www.ncbi.nlm.nih.gov/pubmed/33303746 http://dx.doi.org/10.1038/s41597-020-00773-y |
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author | Jakob, Carolin E. M. Kohlmayer, Florian Meurers, Thierry Vehreschild, Jörg Janne Prasser, Fabian |
author_facet | Jakob, Carolin E. M. Kohlmayer, Florian Meurers, Thierry Vehreschild, Jörg Janne Prasser, Fabian |
author_sort | Jakob, Carolin E. M. |
collection | PubMed |
description | The Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) is a European registry for studying the epidemiology and clinical course of COVID-19. To support evidence-generation at the rapid pace required in a pandemic, LEOSS follows an Open Science approach, making data available to the public in real-time. To protect patient privacy, quantitative anonymization procedures are used to protect the continuously published data stream consisting of 16 variables on the course and therapy of COVID-19 from singling out, inference and linkage attacks. We investigated the bias introduced by this process and found that it has very little impact on the quality of output data. Current laws do not specify requirements for the application of formal anonymization methods, there is a lack of guidelines with clear recommendations and few real-world applications of quantitative anonymization procedures have been described in the literature. We therefore believe that our work can help others with developing urgently needed anonymization pipelines for their projects. |
format | Online Article Text |
id | pubmed-7729909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77299092020-12-17 Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19 Jakob, Carolin E. M. Kohlmayer, Florian Meurers, Thierry Vehreschild, Jörg Janne Prasser, Fabian Sci Data Article The Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) is a European registry for studying the epidemiology and clinical course of COVID-19. To support evidence-generation at the rapid pace required in a pandemic, LEOSS follows an Open Science approach, making data available to the public in real-time. To protect patient privacy, quantitative anonymization procedures are used to protect the continuously published data stream consisting of 16 variables on the course and therapy of COVID-19 from singling out, inference and linkage attacks. We investigated the bias introduced by this process and found that it has very little impact on the quality of output data. Current laws do not specify requirements for the application of formal anonymization methods, there is a lack of guidelines with clear recommendations and few real-world applications of quantitative anonymization procedures have been described in the literature. We therefore believe that our work can help others with developing urgently needed anonymization pipelines for their projects. Nature Publishing Group UK 2020-12-10 /pmc/articles/PMC7729909/ /pubmed/33303746 http://dx.doi.org/10.1038/s41597-020-00773-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Jakob, Carolin E. M. Kohlmayer, Florian Meurers, Thierry Vehreschild, Jörg Janne Prasser, Fabian Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19 |
title | Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19 |
title_full | Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19 |
title_fullStr | Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19 |
title_full_unstemmed | Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19 |
title_short | Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19 |
title_sort | design and evaluation of a data anonymization pipeline to promote open science on covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729909/ https://www.ncbi.nlm.nih.gov/pubmed/33303746 http://dx.doi.org/10.1038/s41597-020-00773-y |
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