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

Author

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  • Alexander Bernier

    (McGill University, Faculty of Medicine)

  • Hanshi Liu

    (McGill University, Faculty of Medicine)

  • Bartha Maria Knoppers

    (McGill University, Faculty of Medicine)

Abstract

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.

Suggested Citation

  • Alexander Bernier & Hanshi Liu & Bartha Maria Knoppers, 2021. "Computational tools for genomic data de-identification: facilitating data protection law compliance," Nature Communications, Nature, vol. 12(1), pages 1-3, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27219-2
    DOI: 10.1038/s41467-021-27219-2
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    References listed on IDEAS

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    1. Christoph Ziegenhain & Rickard Sandberg, 2021. "BAMboozle removes genetic variation from human sequence data for open data sharing," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
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