A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example

TL;DR

This paper explores secure algorithms for vertical federated learning, specifically using secure logistic regression, to enable collaborative model training while preserving data privacy through encryption.

Contribution

It introduces a secure algorithm framework for vertical federated learning, demonstrating how to perform logistic regression securely in an encrypted domain.

Findings

Secure logistic regression can be effectively implemented in vertical federated learning.

The proposed methods protect data privacy during collaborative training.

Experimental results show comparable performance to non-secure models.

Abstract

After entering the era of big data, more and more companies build services with machine learning techniques. However, it is costly for companies to collect data and extract helpful handcraft features on their own. Although it is a way to combine with other companies' data for boosting the model's performance, this approach may be prohibited by laws. In other words, finding the balance between sharing data with others and keeping data from privacy leakage is a crucial topic worthy of close attention. This paper focuses on distributed data and conducts secure model training tasks on a vertical federated learning scheme. Here, secure implies that the whole process is executed in the encrypted domain. Therefore, the privacy concern is released.