Article | SHI 2015, Proceedings from The 13th Scandinavien Conference on Health Informatics, June 15-17, 2015, Tromsø, Norway | Emnet: a System for Privacy-Preserving Statistical Computing on Distributed Health Data
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Title:
Emnet: a System for Privacy-Preserving Statistical Computing on Distributed Health Data
Author:
Meskerem Asfaw Hailemichæl: Department of Computer Science, UiT The Arctic University of Norway, Norway Kassaye Yitbarek Yigzaw: Department of Computer Science, UiT The Arctic University of Norway, Norway Johan Gustav Bellika: Department of Clinical Medicine, UiT The Arctic University of Norway, Norway / Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Norway
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Full text (pdf)
Year:
2015
Conference:
SHI 2015, Proceedings from The 13th Scandinavien Conference on Health Informatics, June 15-17, 2015, Tromsø, Norway
Issue:
115
Article no.:
006
Pages:
33-40
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2015-06-26
ISBN:
978-91-7685-985-8
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Reuse of health data for epidemiological and health services research have enormous benefits for individuals and society. However, patients’ and health institutions’ have privacy concerns. Yet, the commonly used de-identification and consentbased privacy-preserving methods have limitations. In this paper we described three generic requirements for privacy-preserving statistical computing on distributed health data. Then, we described building blocks for implementation on horizontally partitioned data. For each research project, a set of participant health institutions locally store data extracts for the researchers’ criteria. The data across the institutions collectively make the project data, which we refer to as virtual dataset. We decomposed count, mean, standard deviation, variance, covariance, and Pearson’s r into summation forms and described as an abstract computation graph, where subcomputations are nodes. Generic APIs that can be invoked at runtime to execute a node against a virtual dataset are defined. Then we described a proof of concept implementation called Emnet. Emnet demonstrates that horizontally partitioned data reuse can be possible while preserving patients’ and institutions’ privacy. More statistical analyses can easily be included into Emnet a

Keywords: Computation Graph; Data Reuse; EHR; Health Information System; Health Services Research; Privacy; Secondary Use; Statistical Computing; Secure Multi-party Computation; Secure Summation; Virtual Dataset

SHI 2015, Proceedings from The 13th Scandinavien Conference on Health Informatics, June 15-17, 2015, Tromsø, Norway

Author:
Meskerem Asfaw Hailemichæl, Kassaye Yitbarek Yigzaw, Johan Gustav Bellika
Title:
Emnet: a System for Privacy-Preserving Statistical Computing on Distributed Health Data
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SHI 2015, Proceedings from The 13th Scandinavien Conference on Health Informatics, June 15-17, 2015, Tromsø, Norway

Author:
Meskerem Asfaw Hailemichæl, Kassaye Yitbarek Yigzaw, Johan Gustav Bellika
Title:
Emnet: a System for Privacy-Preserving Statistical Computing on Distributed Health Data
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