Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)

BACKGROUND: In biomedical research, data sharing and information exchange are very important for improving quality of care, accelerating discovery, and promoting the meaningful secondary use of clinical data. A big concern in biomedical data sharing is the protection of patient privacy because inappropriate information leakage can put patient privacy at risk. METHODS: In this study, we deployed a grid logistic regression framework based on Secure Multi-party Computation (SMAC-GLORE). Unlike our previous work in GLORE, SMAC-GLORE protects not only patient-level data, but also all the intermediary information exchanged during the model-learning phase. RESULTS: The experimental results demonstrate the feasibility of secure distributed logistic regression across multiple institutions without sharing patient-level data. CONCLUSIONS: In this study, we developed a circuit-based SMAC-GLORE framework. The proposed framework provides a practical solution for secure distributed logistic regression model learning.