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Exploring Biases for Privacy-Preserving Phonetic Matching

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New Trends in Database and Information Systems (ADBIS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1850))

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Abstract

Soundex has been proposed as an alternative for Privacy-Preserving Record Linkage featuring significant performance in terms of result quality and efficiency, without, however, the corresponding attention to evaluate its performance with respect to fairness. In this paper, we focus on race and gender biases and examine the behavior of Soundex using a real world dataset and Apache Spark for processing. We compare these results with two other well known phonetic algorithms, namely NYSIIS and Metaphone. Our evaluation indicates that no biases are induced with respect to gender. On the other hand, regarding race, biases have been observed for all examined algorithms.

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Notes

  1. 1.

    https://okeanos-knossos.grnet.gr/home/.

  2. 2.

    Available at: https://www.mit.edu/~ecprice/wordlist.10000.

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Authors and Affiliations

  1. Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

    Alexandros Karakasidis & Georgia Koloniari

Authors
  1. Alexandros Karakasidis
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  2. Georgia Koloniari
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Corresponding author

Correspondence to Alexandros Karakasidis .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Abelló

  2. University of Ioannina, Ioannina, Greece

    Panos Vassiliadis

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Oscar Romero

  4. Poznań University of Technology, Poznan, Poland

    Robert Wrembel

  5. University of Paris-Saclay, Gif-sur-Yvette, France

    Francesca Bugiotti

  6. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Johann Gamper

  7. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas Solar

  8. University of Calabria, Rende, Italy

    Ester Zumpano

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Karakasidis, A., Koloniari, G. (2023). Exploring Biases for Privacy-Preserving Phonetic Matching. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_9

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  • DOI: https://doi.org/10.1007/978-3-031-42941-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42940-8

  • Online ISBN: 978-3-031-42941-5

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