Sparse Private LASSO Logistic Regression

Thumbnail Image

Files

2304.12429.pdf (322.07 KB)

Links to Files

https://arxiv.org/abs/2304.12429

Permanent Link

https://doi.org/10.48550/arXiv.2304.12429
 http://hdl.handle.net/11603/28048

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0003-1026-5734
 https://orcid.org/0000-0002-9900-1972

Date

2023-04-24

Type of Work

journal articles
preprints
Text

Department

Program

Citation of Original Publication

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

Subjects

Abstract

LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions. Differentially private versions of LASSO logistic regression have been developed, but generally produce dense solutions, reducing the intrinsic utility of the LASSO penalty. In this paper, we present a differentially private method for sparse logistic regression that maintains hard zeros. Our key insight is to first train a non-private LASSO logistic regression model to determine an appropriate privatized number of non-zero coefficients to use in final model selection. To demonstrate our method’s performance, we run experiments on synthetic and real-world datasets.