Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing

[Benjamin-Lee]

I'm surprised that we haven't cited this paper in Tip 10 yet.

Abstract

We initiate the study of privacy in pharmacogenetics, wherein machine learning models are used to guide med- ical treatments based on a patient’s genotype and back- ground. Performing an in-depth case study on privacy in personalized warfarin dosing, we show that suggested models carry privacy risks, in particular because attack- ers can perform what we call model inversion: an at- tacker, given the model and some demographic infor- mation about a patient, can predict the patient’s genetic markers. As differential privacy (DP) is an oft-proposed solu- tion for medical settings such as this, we evaluate its ef- fectiveness for building private versions of pharmacoge- netic models. We show that DP mechanisms prevent our model inversion attacks when the privacy budget is care- fully selected. We go on to analyze the impact on utility by performing simulated clinical trials with DP dosing models. We find that for privacy budgets effective at pre- venting attacks, patients would be exposed to increased risk of stroke, bleeding events, and mortality. We con- clude that current DP mechanisms do not simultaneously improve genomic privacy while retaining desirable clin- ical efficacy, highlighting the need for new mechanisms that should be evaluated in situ using the general method- ology introduced by our work.