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Privacy-Preserving Spatio-Temporal Patient Data Publishing

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Database and Expert Systems Applications (DEXA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12392))

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Abstract

As more data become available to the public, the value of information seems to be diminishing with concern over what constitute privacy of individual. Despite benefit to data publishing, preserving privacy of individuals remains a major concern because linking of data from heterogeneous source become easier due to the vast availability of artificial intelligence tools. In this paper, we focus on preserving privacy of spatio-temporal data publishing. Specifically, we present a framework consisting of (i) a 5-level temporal hierarchy to protect the temporal attributes and (ii) temporal representative point (TRP) differential privacy to protect the spatial attributes. Evaluation results on big datasets show that our framework keeps a good balance of utility and privacy. To a further extent, our solution is expected be extendable for privacy-preserving data publishing for the spatio-temporal data of coronavirus disease 2019 (COVID-19) patients.

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Acknowledgements

This work is partially supported by NSERC (Canada) and University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Anifat M. Olawoyin, Carson K. Leung & Ratna Choudhury

Authors
  1. Anifat M. Olawoyin
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  2. Carson K. Leung
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  3. Ratna Choudhury
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Corresponding author

Correspondence to Carson K. Leung .

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Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. IFS, Vienna University of Technology, Vienna, Wien, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Olawoyin, A.M., Leung, C.K., Choudhury, R. (2020). Privacy-Preserving Spatio-Temporal Patient Data Publishing. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12392. Springer, Cham. https://doi.org/10.1007/978-3-030-59051-2_28

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  • DOI: https://doi.org/10.1007/978-3-030-59051-2_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59050-5

  • Online ISBN: 978-3-030-59051-2

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