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k m-Anonymity for Continuous Data Using Dynamic Hierarchies

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Privacy in Statistical Databases (PSD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8744))

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Abstract

Many organizations, enterprises or public services collect and manage personal data of individuals. These data contain knowledge that is of substantial value for scientists and market experts, but carelessly disseminating them can lead to significant privacy breaches, as they might reveal financial, medical or other personal information. Several anonymization methods have been proposed to allow the privacy preserving sharing of datasets with personal information. Anonymization techniques provide a trade-off between the strength of the privacy guarantee and the quality of the anonymized dataset. In this work we focus on the anonymization of sets of values from continuous domains, e.g., numerical data, and we provide a method for protecting the anonymized data from attacks against identity disclosure. The main novelty of our approach is that instead of using a fixed, given generalization hierarchy, we let the anonymization algorithm decide how different values will be generalized. The benefit of our approach is twofold: a) we are able to generalize datasets without requiring an expert to define the hierarchy and b) we limit the information loss, since the proposed algorithm is able to limit the scope of the generalization. We provide a series of experiments that demonstrate the gains in terms of information quality of our algorithm compared to the state-of-the-art.

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Author information

Authors and Affiliations

  1. National Technical University of Athens, Greece

    Olga Gkountouna, Sotiris Angeli & Yannis Vassiliou

  2. Institute for the Management of Information Systems (IMIS), Athens, Greece

    Olga Gkountouna, Athanasios Zigomitros & Manolis Terrovitis

  3. University of Piraeus, Greece

    Athanasios Zigomitros

Authors
  1. Olga Gkountouna
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  2. Sotiris Angeli
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  3. Athanasios Zigomitros
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  4. Manolis Terrovitis
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  5. Yannis Vassiliou
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering and Mathematics, UNESCO Chair in Data Privacy, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007, Tarragona, Catalonia

    Josep Domingo-Ferrer

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Gkountouna, O., Angeli, S., Zigomitros, A., Terrovitis, M., Vassiliou, Y. (2014). k m-Anonymity for Continuous Data Using Dynamic Hierarchies. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-11257-2_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11256-5

  • Online ISBN: 978-3-319-11257-2

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