Harnessing The Potential Of Shared Data In A Secure, Inclusive, And Resilient Manner Via Multi-key Homomorphic Encryption

The Multi-Key Homomorphic Encryption Logistic Regression (MK-HELR) algorithm allows for secure and dynamic collaborative learning from multiple data sources, while preserving the privacy of individual data. This approach is demonstrated using the AI4I public predictive maintenance dataset and has potential for further research in multi-party learning scenarios.