Secure Multi pArty Computation Grid LOgistic REgression (SMAC GLORE)
BackgroundIn 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.MethodsIn 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.ResultsThe experimental results demonstrate the feasibility of secure distributed logistic regression across multiple institutions without sharing patient level data.ConclusionsIn this study, we developed a circuit based SMAC GLORE framework. The proposed framework provides a practical solution for secure distributed logistic regression model learning.
BMC Medical Informatics and Decision Making