Harnessing the potential of shared data in a secure, inclusive, and resilient manner via multi-key homomorphic encryption

This paper develops a multi-party framework tailored for multiple data contributors seeking machine learning insights from combined data sources. Grounded in statistical learning principles, we introduce the multi-key homomorphic encryption logistic regression algorithm, designed to execute logistic regression on encrypted multiple data.