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Securizing data linkage in french public statistics

Administrative records in France, especially medical and social records, have huge potential for statistical studies. The NIR (a national identifier) is widely used in medico-social administrations, and this would theoretically provide considerable scope for data matching, on condition that the legi...

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Autores principales: Guesdon, Maxence, Benzenine, Eric, Gadouche, Kamel, Quantin, Catherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5053094/
https://www.ncbi.nlm.nih.gov/pubmed/27716178
http://dx.doi.org/10.1186/s12911-016-0366-4
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author Guesdon, Maxence
Benzenine, Eric
Gadouche, Kamel
Quantin, Catherine
author_facet Guesdon, Maxence
Benzenine, Eric
Gadouche, Kamel
Quantin, Catherine
author_sort Guesdon, Maxence
collection PubMed
description Administrative records in France, especially medical and social records, have huge potential for statistical studies. The NIR (a national identifier) is widely used in medico-social administrations, and this would theoretically provide considerable scope for data matching, on condition that the legislation on such matters was respected. The law, however, forbids the processing of non-anonymized medical data, thus making it difficult to carry out studies that require several sources of social and medical data. We would like to benefit from computer techniques introduced since the 70 s to provide safe linkage of anonymized files, to release the current constraints of such procedures. We propose an organization and a data workflow, based on hashing and cyrptographic techniques, to strongly compartmentalize identifying and not-identifying data. The proposed method offers a strong control over who is in possession of which information, using different hashing keys for each linkage. This allows to prevent unauthorized linkage of data, to protect anonymity, by preventing cumulation of not-identifying data which can become identifying data when linked. Our proposal would make it possible to conduct such studies more easily, more regularly and more precisely while preserving a high enough level of anonymity. The main obstacle to setting up such a system, in our opinion, is not technical, but rather organizational in that it is based on the existence of a Key-Management Authority.
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spelling pubmed-50530942016-10-18 Securizing data linkage in french public statistics Guesdon, Maxence Benzenine, Eric Gadouche, Kamel Quantin, Catherine BMC Med Inform Decis Mak Debate Administrative records in France, especially medical and social records, have huge potential for statistical studies. The NIR (a national identifier) is widely used in medico-social administrations, and this would theoretically provide considerable scope for data matching, on condition that the legislation on such matters was respected. The law, however, forbids the processing of non-anonymized medical data, thus making it difficult to carry out studies that require several sources of social and medical data. We would like to benefit from computer techniques introduced since the 70 s to provide safe linkage of anonymized files, to release the current constraints of such procedures. We propose an organization and a data workflow, based on hashing and cyrptographic techniques, to strongly compartmentalize identifying and not-identifying data. The proposed method offers a strong control over who is in possession of which information, using different hashing keys for each linkage. This allows to prevent unauthorized linkage of data, to protect anonymity, by preventing cumulation of not-identifying data which can become identifying data when linked. Our proposal would make it possible to conduct such studies more easily, more regularly and more precisely while preserving a high enough level of anonymity. The main obstacle to setting up such a system, in our opinion, is not technical, but rather organizational in that it is based on the existence of a Key-Management Authority. BioMed Central 2016-10-06 /pmc/articles/PMC5053094/ /pubmed/27716178 http://dx.doi.org/10.1186/s12911-016-0366-4 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Debate
Guesdon, Maxence
Benzenine, Eric
Gadouche, Kamel
Quantin, Catherine
Securizing data linkage in french public statistics
title Securizing data linkage in french public statistics
title_full Securizing data linkage in french public statistics
title_fullStr Securizing data linkage in french public statistics
title_full_unstemmed Securizing data linkage in french public statistics
title_short Securizing data linkage in french public statistics
title_sort securizing data linkage in french public statistics
topic Debate
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5053094/
https://www.ncbi.nlm.nih.gov/pubmed/27716178
http://dx.doi.org/10.1186/s12911-016-0366-4
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