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Post-processing Methods for High Quality Privacy-Preserving Record Linkage

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Data Privacy Management, Cryptocurrencies and Blockchain Technology (DPM 2018, CBT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11025))

Abstract

Privacy-preserving record linkage (PPRL) supports the integration of person-related data from different sources while protecting the privacy of individuals by encoding sensitive information needed for linkage. The use of encoded data makes it challenging to achieve high linkage quality in particular for dirty data containing errors or inconsistencies. Moreover, person-related data is often dense, e.g., due to frequent names or addresses, leading to high similarities for non-matches. Both effects are hard to deal with in common PPRL approaches that rely on a simple threshold-based classification to decide whether a record pair is considered to match. In particular, dirty or dense data likely lead to many multi-links where persons are wrongly linked to more than one other person. Therefore, we propose the use of post-processing methods for resolving multi-links and outline three possible approaches. In our evaluation using large synthetic and real datasets we compare these approaches with each other and show that applying post-processing is highly beneficial and can significantly increase linkage quality in terms of both precision and F-measure.

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Notes

  1. 1.

    https://www.destatis.de/DE/Methoden/Zensus_/Zensus.html.

  2. 2.

    http://www.ncsbe.gov/.

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Author information

Authors and Affiliations

  1. Database Group, University of Leipzig, Leipzig, Germany

    Martin Franke, Ziad Sehili, Marcel Gladbach & Erhard Rahm

Authors
  1. Martin Franke
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  2. Ziad Sehili
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  3. Marcel Gladbach
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  4. Erhard Rahm
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Corresponding author

Correspondence to Martin Franke .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Evry, France

    Joaquin Garcia-Alfaro

  2. Enginyeria de la Informacio I de les Com, Universitat Autonoma de Barcelona, Bellaterra, Spain

    Jordi Herrera-Joancomartí

  3. Università degli Studi di Milano, Crema, Italy

    Giovanni Livraga

  4. Universidad de Málaga, Málaga, Spain

    Ruben Rios

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Franke, M., Sehili, Z., Gladbach, M., Rahm, E. (2018). Post-processing Methods for High Quality Privacy-Preserving Record Linkage. In: Garcia-Alfaro, J., Herrera-Joancomartí, J., Livraga, G., Rios, R. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2018 2018. Lecture Notes in Computer Science(), vol 11025. Springer, Cham. https://doi.org/10.1007/978-3-030-00305-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-00305-0_19

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