Privacy-preserving Logistic Regression with Secret Sharing

TL;DR

This paper presents secret sharing-based privacy-preserving logistic regression protocols using secure MPC, enabling multiple data owners to collaboratively train accurate models without compromising data privacy.

Contribution

It introduces iterative algorithms for federated privacy-preserving logistic regression using secret sharing and secure MPC, improving efficiency and accuracy over existing methods.

Findings

Protocols are highly efficient and accurate.

Methods can handle large datasets from multiple sources.

Experimental results validate the approach's effectiveness.

Abstract

Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Research that collects and combines datasets from various data custodians and jurisdictions can excessively benefit from the increased statistical power to support their analyzing goals. However, combining data from these various sources creates significant privacy concerns that need to be addressed. In this paper, we proposed secret sharing-based privacy-preserving logistic regression protocols using the Newton-Raphson method. Our proposed approaches are based on secure Multi-Party Computation (MPC) with different security settings to analyze data owned by several data holders. We conducted experiments on both synthetic data and real-world datasets and compared the efficiency and accuracy of them with those of an ordinary logistic regression model. Experimental results demonstrate that the proposed protocols are highly efficient and accurate. This study introduces iterative algorithms to simplify the federated training a logistic regression model in a privacy-preserving manner. Our implementation results show that our improved method can handle large datasets used in securely training a logistic regression from multiple sources.