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

Alexander Bernier; Hanshi Liu; Bartha Maria Knoppers

doi:10.1038/s41467-021-27219-2

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

Nat Commun. 2021 Nov 29;12(1):6949. doi: 10.1038/s41467-021-27219-2.

Authors

Alexander Bernier¹, Hanshi Liu², Bartha Maria Knoppers²

Affiliations

¹ Centre of Genomics and Policy, McGill University, Faculty of Medicine, 740, avenue Dr. Penfield, suite 5200, Montreal, QC, H3A 0G1, Canada. alexander.bernier@mcgill.ca.
² Centre of Genomics and Policy, McGill University, Faculty of Medicine, 740, avenue Dr. Penfield, suite 5200, Montreal, QC, H3A 0G1, Canada.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computer Security / standards*
Confidentiality / legislation & jurisprudence*
Genomics / legislation & jurisprudence
Humans