Machine learning and genomics: precision medicine vs. patient privacy

TL;DR

This paper reviews how machine learning advances in genomics for precision medicine pose privacy risks and discusses technical, legal, and ethical solutions to protect patient data while enabling research.

Contribution

It provides a comprehensive overview of privacy breach mechanisms and recent computational data protection methods in genomic research.

Findings

Privacy breaches can occur through various data re-identification techniques.

Recent developments include cryptographic and differential privacy methods.

Combining technical solutions with legal frameworks enhances data security.

Abstract

Machine learning can have 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.