Title |
Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)
|
---|---|
Published in |
BMC Medical Informatics and Decision Making, July 2016
|
DOI | 10.1186/s12911-016-0316-1 |
Pubmed ID | |
Authors |
Haoyi Shi, Chao Jiang, Wenrui Dai, Xiaoqian Jiang, Yuzhe Tang, Lucila Ohno-Machado, Shuang Wang |
Abstract |
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. 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. The experimental results demonstrate the feasibility of secure distributed logistic regression across multiple institutions without sharing patient-level data. 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. |
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Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 4 | 13% |
Student > Master | 3 | 9% |
Other | 2 | 6% |
Professor > Associate Professor | 2 | 6% |
Other | 5 | 16% |
Unknown | 7 | 22% |
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Unspecified | 1 | 3% |
Other | 2 | 6% |
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