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

07/30/2014
by   Fei Yu, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/24/2023

Sparse Private LASSO Logistic Regression

LASSO regularized logistic regression is particularly useful for its bui...
research
02/19/2022

Differentially Private Regression with Unbounded Covariates

We provide computationally efficient, differentially private algorithms ...
research
09/19/2023

Striking a Balance: An Optimal Mechanism Design for Heterogenous Differentially Private Data Acquisition for Logistic Regression

We investigate the problem of performing logistic regression on data col...
research
09/13/2019

A Knowledge Transfer Framework for Differentially Private Sparse Learning

We study the problem of estimating high dimensional models with underlyi...
research
03/18/2023

The Challenge of Differentially Private Screening Rules

Linear L_1-regularized models have remained one of the simplest and most...
research
10/05/2022

Learning from aggregated data with a maximum entropy model

Aggregating a dataset, then injecting some noise, is a simple and common...
research
06/12/2019

(A) Data in the Life: Authorship Attribution of Lennon-McCartney Songs

The songwriting duo of John Lennon and Paul McCartney, the two founding ...

Please sign up or login with your details

Forgot password? Click here to reset