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FOIN: a nominative information occultation function.

Authors: G, Trouessin; F A, Allaert;

FOIN: a nominative information occultation function.

Abstract

This article is intended to show that a great amount of work has to (and will) be done in terms of security for the healthcare information system community. Some very promising results have already been obtained in France, especially at CNAMTS, for providing the required security and privacy of nominative information in the context of future electronic exchanges between private/public hospital and CNAMTS, the french biggest mandatory healthcare insurance. FOIN, a robust anonymisation function, is just an example of such recent progress that traduces the french national engagement towards more flexible interconnection between various authorised healthcare information systems in close conjunction with more and more reliable and secure procedural and technical requirements. Firstly, the legal and medical needs for so strong anonymisation functions are described; secondly, the most important security requirements of these sensible functions are detailed and, lastly, the main security and implementation features of FOIN are globally indicated.

Keywords

Medical Records Systems, Computerized, Humans, France, Computing Methodologies, Algorithms, Computer Security, Confidentiality, Information Systems

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Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
The total number of citations received by all articles of the researcher of interest.
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Average
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