Machine learning and genomics : precision medicine versus patient privacy

© 2017 The Author(s)..

Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

Medienart:

E-Artikel

Erscheinungsjahr:

2018

Erschienen:

2018

Enthalten in:

Zur Gesamtaufnahme - volume:376

Enthalten in:

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences - 376(2018), 2128 vom: 13. Sept.

Sprache:

Englisch

Beteiligte Personen:

Azencott, C-A [VerfasserIn]

Links:

Volltext

Themen:

Cryptographic hardware
Differential privacy
Genomics privacy
Homomorphic encryption
Journal Article
Precision medicine
Review
Secure multi-party computing

Anmerkungen:

Date Completed 21.06.2019

Date Revised 21.06.2019

published: Print

Citation Status MEDLINE

doi:

10.1098/rsta.2017.0350

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM287217876