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

I'm surprised that we haven't cited [this paper](https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-fredrikson-privacy.pdf) 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.

Start by locating Tip 10 in the manuscript and checking how its existing references are formatted. Review the linked paper, add it to the appropriate citation and bibliography entries, and verify that Tip 10 renders with the new reference correctly.

Written by the indexing model from the issue text.