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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Yu, F., Rybar, M., Uhler, C., Fienberg, S.E. (2014). Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_14
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DOI: https://doi.org/10.1007/978-3-319-11257-2_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11256-5
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