  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.
