Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
This work addresses privacy concerns in GWAS databases for researchers and healthcare professionals, though it is incremental as it applies existing differential privacy techniques to a specific regression method.
The authors tackled the problem of privacy-preserving genome-wide association studies (GWAS) by developing a differentially private method for penalized logistic regression with elastic-net regularization, enabling secure analysis of GWAS data while maintaining utility.
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.