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

ABSTRACT: In this manuscript, we develop 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 (MK-HELR) algorithm, designed to execute logistic regression on encrypted multi-party data. Given that models built on aggregated datasets often demonstrate superior generalization capabilities, our approach offers data contributors the collective strength of shared data while ensuring their original data remains private due to encryption. Apart from facilitating logistic regression on combined encrypted data from diverse sources, this algorithm creates a collaborative learning environment with dynamic membership. Notably,

SUBMITTER: Kang DHE 

PROVIDER: S-EPMC11637100 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature