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

arXiv: https://arxiv.org/abs/1407.8067 · HTML: https://mlsanity.com/paper/1407.8067/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2014.

## Built on by (most cited first, 3 of 3)

- [Differentially private inference via noisy optimization](https://mlsanity.com/paper/2103.11003/) (2021)
- [Sparse Private LASSO Logistic Regression](https://mlsanity.com/paper/2304.12429/) (2023)
- [Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-purpose Regularizer and Privacy Budget Retrieval and Recycling](https://mlsanity.com/paper/2110.08676/) (2021)

## Abstract (arXiv)

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.