title: Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
abstract: We initiate the study of privacy in pharmacogenetics, wherein machine learning models are used to guide medical treatments based on a patient's genotype and background. Performing an in-depth case study on privacy in personalized warfarin dosing, we show that suggested models carry privacy risks, in particular because attackers can perform what we callmodel inversion: an attacker, given the model and some demographic information about a patient, can predict the patient's genetic markers. As differential privacy (DP) is an oft-proposed solution for medical settings such as this, we evaluate its effectiveness for building private versions of pharmacogenetic models. We show thatDP mechanisms prevent our model inversion attacks when the privacy budget is carefully 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 preventing attacks,patients would be exposed to increased risk of stroke, bleeding events, and mortality. We conclude thatcurrentDP mechanisms do not simultaneously improve genomic privacy while retaining desirable clinical efficacy, highlighting the need for new mechanisms that should be evaluatedin situusing the general methodology introduced by our work.
authors.affiliation: University of Wisconsin.
authors.forename: Matthew
authors.initials: M
authors.lastname: Fredrikson
authors.affiliation: University of Wisconsin.
authors.forename: Eric
authors.initials: E
authors.lastname: Lantz
authors.affiliation: University of Wisconsin.
authors.forename: Somesh
authors.initials: S
authors.lastname: Jha
authors.affiliation: Marshfield Clinic Research Foundation.
authors.forename: Simon
authors.initials: S
authors.lastname: Lin
authors.affiliation: University of Wisconsin.
authors.forename: David
authors.initials: D
authors.lastname: Page
authors.affiliation: University of Wisconsin.
authors.forename: Thomas
authors.initials: T
authors.lastname: Ristenpart