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BMC Med Inform Decis Mak. 2016 Jul 25;16 Suppl 3:89. doi: 10.1186/s12911-016-0316-1.

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

Author information

1
Department of Biomedical Informatics, University of California, San Diego, CA, 92093, USA.
2
Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13210, USA.
3
School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, 74135, USA.
4
Department of Biomedical Informatics, University of California, San Diego, CA, 92093, USA. shw070@ucsd.edu.

Abstract

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.

PMID:
27454168
PMCID:
PMC4959358
DOI:
10.1186/s12911-016-0316-1
[Indexed for MEDLINE]
Free PMC Article

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