A flexible approach to distributed data anonymization

Journal of Biomedical Informatics
Florian KohlmayerKlaus A Kuhn

Abstract

Sensitive biomedical data is often collected from distributed sources, involving different information systems and different organizational units. Local autonomy and legal reasons lead to the need of privacy preserving integration concepts. In this article, we focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data. We present a flexible solution for anonymizing distributed data in the semi-honest model. Prior to the anonymization procedure, an encrypted global view of the dataset is constructed by means of a secure multi-party computing (SMC) protocol. This global representation can then be anonymized. Our approach is not limited to specific anonymization algorithms but provides pre- and postprocessing for a broad spectrum of algorithms and many privacy criteria. We present an extensive analytical and experimental evaluation and discuss which types of methods and criteria are supported. Our prototype demonstrates the approach by implementing k-anonymity, ℓ-diversity, t-closeness and δ-presence with a globally optimal de-identification method in horizontally and vertically distributed setups. The experiments show that our method provides highly competitive perfo...Continue Reading

References

Aug 2, 2007·The European Respiratory Journal·A Cambon-ThomsenB M Knoppers
Jul 2, 2009·Journal of the American Medical Informatics Association : JAMIA·Khaled El EmamJim Bottomley
Sep 10, 2009·Physiological Genomics·Philip R O PayneChandan K Sen
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May 15, 2012·Science·Jane KayeKazuto Kato
Jun 28, 2013·BMC Medical Research Methodology·Irantzu BarrioIryss-Copd Group

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Citations

May 1, 2016·BMC Medical Informatics and Decision Making·Fabian PrasserKlaus A Kuhn
Jun 4, 2019·Journal of Medical Internet Research·Raphaël ChevrierChristian Lovis

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