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arXiv · 1407.8067

Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases

Fei Yu·Michal Rybar·Caroline Uhler·Stephen E. Fienberg
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Abstract

Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems with convex penalty functions and selecting the penalty parameters by cross-validation. In particular, we focus on penalized logistic regression with elastic-net regularization, a method widely used to in GWAS analyses to identify disease-causing genes. We show how a differentially private procedure for penalized logistic regression with elastic-net regularization can be applied to the analysis of GWAS data and evaluate our method's performance.

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Fei Yu, Michal Rybar, Caroline Uhler, Stephen E. Fienberg. 2014-07-30. Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases. https://arxiv.org/abs/1407.8067

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