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

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...

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Bibliographic Details
Main Authors: David Ha Eun Kang, Duhyeong Kim, Yongsoo Song, Dongwon Lee, Hyesun Kwak, Brian W. Anthony
Format: Article
Language:English
Published: Nature Portfolio 2024-06-01
Series:Scientific Reports
Subjects:
Statistical learning
Data privacy
Multi-key homomorphic encryption
Multi-party collaborative learning
Dynamic membership
Adaptive machine learning systems
Online Access:https://doi.org/10.1038/s41598-024-63393-1
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https://doi.org/10.1038/s41598-024-63393-1

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