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Privacy preserving distributed computation of community health research data

Permanent link
https://hdl.handle.net/10037/12587
DOI
https://doi.org/10.1016/j.procs.2017.08.319
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Date
2017-09-19
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Andersen, Anders; Saus, Merete
Abstract
Research in community health introduces challenges regarding analysis of the research data. It involves multiple actors in a varity of arenas, and it is often directed towards the local community and children and their families. The legal, ethical and privacy issues involved introduce constraints upon the analysis performed. SNOOP combined with the D2Worm declarative modelling and infrastructure architecture is a promising approach to support a wide range of possible privacy preserving analysis in community health research.
Description
Source at https://doi.org/10.1016/j.procs.2017.08.319 .
Publisher
Elsevier
Citation
Andersen, A. & Saus, M. (2017). Privacy preserving distributed computation of community health research data. Procedia Computer Science, 113, 633-640.
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