Computational tools for genomic data de-identification: facilitating data protection law compliance

In this opinion piece, we discuss why computational tools to limit the identifiability of genomic data are a promising avenue for privacy-preservation and legal compliance. Even where these technologies do not eliminate all residual risk of individual identification, the law may still consider such...

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Detalles Bibliográficos
Autores principales: Bernier, Alexander, Liu, Hanshi, Knoppers, Bartha Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8630085/
https://www.ncbi.nlm.nih.gov/pubmed/34845213
http://dx.doi.org/10.1038/s41467-021-27219-2
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author Bernier, Alexander
Liu, Hanshi
Knoppers, Bartha Maria
author_facet Bernier, Alexander
Liu, Hanshi
Knoppers, Bartha Maria
author_sort Bernier, Alexander
collection PubMed
description In this opinion piece, we discuss why computational tools to limit the identifiability of genomic data are a promising avenue for privacy-preservation and legal compliance. Even where these technologies do not eliminate all residual risk of individual identification, the law may still consider such data anonymised.
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spelling pubmed-86300852021-12-01 Computational tools for genomic data de-identification: facilitating data protection law compliance Bernier, Alexander Liu, Hanshi Knoppers, Bartha Maria Nat Commun Comment In this opinion piece, we discuss why computational tools to limit the identifiability of genomic data are a promising avenue for privacy-preservation and legal compliance. Even where these technologies do not eliminate all residual risk of individual identification, the law may still consider such data anonymised. Nature Publishing Group UK 2021-11-29 /pmc/articles/PMC8630085/ /pubmed/34845213 http://dx.doi.org/10.1038/s41467-021-27219-2 Text en © The Author(s) 2021, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Bernier, Alexander
Liu, Hanshi
Knoppers, Bartha Maria
Computational tools for genomic data de-identification: facilitating data protection law compliance
title Computational tools for genomic data de-identification: facilitating data protection law compliance
title_full Computational tools for genomic data de-identification: facilitating data protection law compliance
title_fullStr Computational tools for genomic data de-identification: facilitating data protection law compliance
title_full_unstemmed Computational tools for genomic data de-identification: facilitating data protection law compliance
title_short Computational tools for genomic data de-identification: facilitating data protection law compliance
title_sort computational tools for genomic data de-identification: facilitating data protection law compliance
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8630085/
https://www.ncbi.nlm.nih.gov/pubmed/34845213
http://dx.doi.org/10.1038/s41467-021-27219-2
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