Achieving Both Valid and Secure Logistic Regression Analysis on Aggregated Data from Different Private Sources

TL;DR

This paper introduces a secure protocol for logistic regression on distributed private data, ensuring both privacy and accuracy, with practical demonstrations and performance improvements.

Contribution

It develops a novel secure computation protocol for logistic regression that prevents data leakage and enhances computational efficiency.

Findings

Protocol accurately computes logistic regression parameters

Demonstrates practical feasibility with real survey data

Achieves faster computation of logistic functions

Abstract

Preserving the privacy of individual databases when carrying out statistical calculations has a long history in statistics and had been the focus of much recent attention in machine learning In this paper, we present a protocol for computing logistic regression when the data are held by separate parties without actually combining information sources by exploiting results from the literature on multi-party secure computation. We provide only the final result of the calculation compared with other methods that share intermediate values and thus present an opportunity for compromise of values in the combined database. Our paper has two themes: (1) the development of a secure protocol for computing the logistic parameters, and a demonstration of its performances in practice, and (2) and amended protocol that speeds up the computation of the logistic function. We illustrate the nature of the calculations and their accuracy using an extract of data from the Current Population Survey divided between two parties.