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

BMC Medical Informatics and Decision Making

Research

Wenrui Dai¹  Lucila Ohno-Machado¹  Xiaoqian Jiang¹  Shuang Wang¹  Haoyi Shi²  Chao Jiang³  Yuzhe Tang⁴ 

[1] Department of Biomedical Informatics, University of California, 92093, San Diego, CA, USA;Department of Biomedical Informatics, University of California, 92093, San Diego, CA, USA;Department of Electrical Engineering and Computer Science, Syracuse University, 13210, Syracuse, NY, USA;Department of Biomedical Informatics, University of California, 92093, San Diego, CA, USA;School of Electrical and Computer Engineering, University of Oklahoma, 74135, Tulsa, OK, USA;Department of Electrical Engineering and Computer Science, Syracuse University, 13210, Syracuse, NY, USA;

关键词: Matrix Multiplication;    Matrix Inversion;    Ordinary Logistic Regression;    Oblivious Transfer;    Garble Circuit;   

DOI  :  10.1186/s12911-016-0316-1

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2016

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