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Preserving Institutional Privacy in Distributed binary Logistic Regression.

Publication ,  Conference
Wu, Y; Jiang, X; Ohno-Machado, L
Published in: Amia Annu Symp Proc
2012

Privacy is becoming a major concern when sharing biomedical data across institutions. Although methods for protecting privacy of individual patients have been proposed, it is not clear how to protect the institutional privacy, which is many times a critical concern of data custodians. Built upon our previous work, Grid Binary LOgistic REgression (GLORE)1, we developed an Institutional Privacy-preserving Distributed binary Logistic Regression model (IPDLR) that considers both individual and institutional privacy for building a logistic regression model in a distributed manner. We tested our method using both simulated and clinical data, showing how it is possible to protect the privacy of individuals and of institutions using a distributed strategy.

Duke Scholars

Published In

Amia Annu Symp Proc

EISSN

1942-597X

Publication Date

2012

Volume

2012

Start / End Page

1450 / 1458

Location

United States

Related Subject Headings

  • Privacy
  • Logistic Models
  • Humans
  • Health Facilities
  • Confidentiality
  • Computer Security
  • Biomedical Research
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wu, Y., Jiang, X., & Ohno-Machado, L. (2012). Preserving Institutional Privacy in Distributed binary Logistic Regression. In Amia Annu Symp Proc (Vol. 2012, pp. 1450–1458). United States.
Wu, Yuan, Xiaoqian Jiang, and Lucila Ohno-Machado. “Preserving Institutional Privacy in Distributed binary Logistic Regression.” In Amia Annu Symp Proc, 2012:1450–58, 2012.
Wu Y, Jiang X, Ohno-Machado L. Preserving Institutional Privacy in Distributed binary Logistic Regression. In: Amia Annu Symp Proc. 2012. p. 1450–8.
Wu, Yuan, et al. “Preserving Institutional Privacy in Distributed binary Logistic Regression.Amia Annu Symp Proc, vol. 2012, 2012, pp. 1450–58.
Wu Y, Jiang X, Ohno-Machado L. Preserving Institutional Privacy in Distributed binary Logistic Regression. Amia Annu Symp Proc. 2012. p. 1450–1458.

Published In

Amia Annu Symp Proc

EISSN

1942-597X

Publication Date

2012

Volume

2012

Start / End Page

1450 / 1458

Location

United States

Related Subject Headings

  • Privacy
  • Logistic Models
  • Humans
  • Health Facilities
  • Confidentiality
  • Computer Security
  • Biomedical Research
  • Algorithms