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    Randomized Response and Balanced Bloom Filters for Privacy Preserving Record Linkage

    Access Status
    Fulltext not available
    Authors
    Schnell, Rainer
    Borgs, Christian
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Schnell, R. and Borgs, C. 2017. Randomized Response and Balanced Bloom Filters for Privacy Preserving Record Linkage, 116th IEEE International Conference on Data Mining Workshops (ICDMW 2016), pp. 218-224.
    Source Title
    Proceedings 16th IEEE International Conference on Data Mining Workshops
    Source Conference
    116th IEEE International Conference on Data Mining Workshops (ICDMW 2016)
    DOI
    10.1109/ICDMW.2016.0038
    ISBN
    9781509054725
    School
    Centre for Population Health Research
    URI
    http://hdl.handle.net/20.500.11937/71413
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 IEEE. In most European settings, record linkage across different institutions is based on encrypted personal identifiers-such as names, birthdays, or places of birth-To protect privacy. However, in practice up to 20% of the records may contain errors in identifiers. Thus, exact record linkage on encrypted identifiers usually results in the loss of large subsets of the data. Such losses usually imply biased statistical estimates since the causes of errors might be correlated with the variables of interest in many applications. Over the past 10 years, the field of Privacy Preserving Record Linkage (PPRL) has developed different techniques to link data without revealing the identity of the described entity. However, only few techniques are suitable for applied research with large data bases that include millions of records, which is typical for administrative or medical data bases. Bloom filters were found to be one successful technique for PPRL when large scale applications are concerned. Yet, Bloom filters have been subject to cryptographic attacks. Previous research has shown that the straight application of Bloom filters has a non-zero re-identification risk. We present new results on recently developed techniques defying all known attacks on PPRL Bloom filters. The computationally inexpensive algorithms modify personal identifiers by combining different cryptographic techniques. The paper demonstrates these new algorithms and demonstrates their performance concerning precision, recall, and re-identification risk on large data bases.

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