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    Privacy-preserving record linkage on large real world datasets

    Access Status
    Open access via publisher
    Authors
    Randall, Sean
    Ferrante, Anna
    Boyd, James
    Bauer, J.
    Semmens, James
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Randall, S. and Ferrante, A. and Boyd, J. and Bauer, J. and Semmens, J. 2014. Privacy-preserving record linkage on large real world datasets. Journal of Biomedical Informatics. 50: pp. 205-212.
    Source Title
    Journal of Biomedical Informatics
    DOI
    10.1016/j.jbi.2013.12.003
    ISSN
    1532-0464
    School
    Centre for Population Health
    URI
    http://hdl.handle.net/20.500.11937/30680
    Collection
    • Curtin Research Publications
    Abstract

    Record linkage typically involves the use of dedicated linkage units who are supplied with personally identifying information to determine individuals from within and across datasets. The personally identifying information supplied to linkage units is separated from clinical information prior to release by data custodians. While this substantially reduces the risk of disclosure of sensitive information, some residual risks still exist and remain a concern for some custodians. In this paper we trial a method of record linkage which reduces privacy risk still further on large real world administrative data. The method uses encrypted personal identifying information (bloom filters) in a probability-based linkage framework. The privacy preserving linkage method was tested on ten years of New South Wales (NSW) and Western Australian (WA) hospital admissions data, comprising in total over 26 million records. No difference in linkage quality was found when the results were compared to traditional probabilistic methods using full unencrypted personal identifiers. This presents as a possible means of reducing privacy risks related to record linkage in population level research studies. It is hoped that through adaptations of this method or similar privacy preserving methods, risks related to information disclosure can be reduced so that the benefits of linked research taking place can be fully realised.

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      © 2017 Brown, Ferrante, Randall, Boyd and Semmens. In an era where the volume of structured and unstructured digital data has exploded, there has been an enormous growth in the creation of data about individuals that can ...
    • Application of privacy-preserving techniques in operational record linkage centres
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    • Estimating parameters for probabilistic linkage of privacy-preserved datasets.
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      Background: Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. ...
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