Privacy-enhancing ETL-processes for biomedical data.

Algorithms Biomedical Research Datasets as Topic Humans Privacy
Anonymization Clinical data warehousing Extract Transform Load Privacy

Journal

International journal of medical informatics
ISSN: 1872-8243
Titre abrégé: Int J Med Inform
Pays: Ireland
ID NLM: 9711057

Informations de publication

Date de publication:
06 2019
Historique:
received: 22 06 2018
revised: 06 11 2018
accepted: 06 03 2019
entrez: 29 4 2019
pubmed: 29 4 2019
medline: 29 10 2019
Statut: ppublish

Résumé

Modern data-driven approaches to medical research require patient-level information at comprehensive depth and breadth. To create the required big datasets, information from disparate sources can be integrated into clinical and translational warehouses. This is typically implemented with Extract, Transform, Load (ETL) processes, which access, harmonize and upload data into the analytics platform. Privacy-protection needs careful consideration when data is pooled or re-used for secondary purposes, and data anonymization is an important protection mechanism. However, common ETL environments do not support anonymization, and common anonymization tools cannot easily be integrated into ETL workflows. The objective of the work described in this article was to bridge this gap. Our main design goals were (1) to base the anonymization process on expert-level risk assessment methodologies, (2) to use transformation methods which preserve both the truthfulness of data and its schematic properties (e.g. data types), (3) to implement a method which is easy to understand and intuitive to configure, and (4) to provide high scalability. We designed a novel and efficient anonymization process and implemented a plugin for the Pentaho Data Integration (PDI) platform, which enables integrating data anonymization and re-identification risk analyses directly into ETL workflows. By combining different instances into a single ETL process, data can be protected from multiple threats. The plugin supports very large datasets by leveraging the streaming-based processing model of the underlying platform. We present results of an extensive experimental evaluation and discuss successful applications. Our work shows that expert-level anonymization methodologies can be integrated into ETL workflows. Our implementation is available under a non-restrictive open source license and it overcomes several limitations of other data anonymization tools.

Sections du résumé

BACKGROUND
Modern data-driven approaches to medical research require patient-level information at comprehensive depth and breadth. To create the required big datasets, information from disparate sources can be integrated into clinical and translational warehouses. This is typically implemented with Extract, Transform, Load (ETL) processes, which access, harmonize and upload data into the analytics platform.
OBJECTIVE
Privacy-protection needs careful consideration when data is pooled or re-used for secondary purposes, and data anonymization is an important protection mechanism. However, common ETL environments do not support anonymization, and common anonymization tools cannot easily be integrated into ETL workflows. The objective of the work described in this article was to bridge this gap.
METHODS
Our main design goals were (1) to base the anonymization process on expert-level risk assessment methodologies, (2) to use transformation methods which preserve both the truthfulness of data and its schematic properties (e.g. data types), (3) to implement a method which is easy to understand and intuitive to configure, and (4) to provide high scalability.
RESULTS
We designed a novel and efficient anonymization process and implemented a plugin for the Pentaho Data Integration (PDI) platform, which enables integrating data anonymization and re-identification risk analyses directly into ETL workflows. By combining different instances into a single ETL process, data can be protected from multiple threats. The plugin supports very large datasets by leveraging the streaming-based processing model of the underlying platform. We present results of an extensive experimental evaluation and discuss successful applications.
CONCLUSIONS
Our work shows that expert-level anonymization methodologies can be integrated into ETL workflows. Our implementation is available under a non-restrictive open source license and it overcomes several limitations of other data anonymization tools.

Identifiants

DOI: 10.1016/j.ijmedinf.2019.03.006 PMID: 31029266
pubmed: 31029266
pii: S1386-5056(18)30700-7
doi: 10.1016/j.ijmedinf.2019.03.006
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

72-81

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

Fabian Prasser (F)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany. Electronic address: fabian.prasser@tum.de.

Helmut Spengler (H)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.

Raffael Bild (R)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.

Johanna Eicher (J)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.

Klaus A Kuhn (KA)

Institute of Medical Informatics, Statistics and Epidemiology, University Hospital rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Privacy-enhancing ETL-processes for biomedical data.
questionsmedicales.fr › Disciplines des sciences naturelles › Science › Recherche › Recherche biomédicale › Privacy-enhancing ETL-processes for biomedical data.
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Collecte de données › Jeux de données comme sujet › Privacy-enhancing ETL-processes for biomedical data.
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Privacy-enhancing ETL-processes for biomedical data.
questionsmedicales.fr › Organisations et économie des soins de santé › Contrôle social formel › Législation comme sujet › Vie privée › Privacy-enhancing ETL-processes for biomedical data.