# Machine learning and genomics: precision medicine vs. patient privacy

arXiv: https://arxiv.org/abs/1802.10568 · HTML: https://mlsanity.com/paper/1802.10568/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [A General Framework for Auditing Differentially Private Machine Learning](https://mlsanity.com/paper/2210.08643/) (2022)
- [On Utility and Privacy in Synthetic Genomic Data](https://mlsanity.com/paper/2102.03314/) (2021)
- [Machine Learning Applications for Therapeutic Tasks with Genomics Data](https://mlsanity.com/paper/2105.01171/) (2021)
- [Supervised Machine Learning with Plausible Deniability](https://mlsanity.com/paper/2106.04267/) (2021)

## Abstract (arXiv)

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.