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 call model 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 that DP mechanisms prevent our model inversion attacks when the privacy budget is carefully selected. We

SUBMITTER: Fredrikson M 

PROVIDER: S-EPMC4827719 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature