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A Summary of \(k\)-Degree Anonymous Methods for Privacy-Preserving on Networks

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Advanced Research in Data Privacy

Part of the book series: Studies in Computational Intelligence ((SCI,volume 567))

Abstract

In recent years there has been a significant raise in the use of graph-formatted data. For instance, social and healthcare networks present relationships among users, revealing interesting and useful information for researches and other third-parties. Notice that when someone wants to publicly release this information it is necessary to preserve the privacy of users who appear in these networks. Therefore, it is essential to implement an anonymization process in the data in order to preserve users’ privacy. Anonymization of graph-based data is a problem which has been widely studied last years and several anonymization methods have been developed. In this chapter we summarize some methods for privacy-preserving on networks, focusing on methods based on the \(k\)-anonymity model. We also compare the results of some \(k\)-degree anonymous methods on our experimental set up, by evaluating the data utility and the information loss on real networks.

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Notes

  1. 1.

    Source code available at: http://deic.uab.cat/~jcasas/.

  2. 2.

    Refer footnote 1.

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Acknowledgments

This work was partly funded by the Spanish Government through projects TIN2011-27076-C03-02 “CO-PRIVACY” and CONSOLIDER INGENIO 2010 CSD2007-0004 “ARES”.

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Authors and Affiliations

  1. Universitat Oberta de Catalunya, Barcelona, Spain

    Jordi Casas-Roma

  2. Universitat Autònoma de Barcelona, Bellaterra, Spain

    Jordi Herrera-Joancomartí

  3. Institut d’Investigació en Intel·ligència Artificial, Consejo Superior de Investigaciones Científicas Campus de la UAB, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

Authors
  1. Jordi Casas-Roma
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  2. Jordi Herrera-Joancomartí
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  3. Vicenç Torra
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Corresponding author

Correspondence to Jordi Casas-Roma .

Editor information

Editors and Affiliations

  1. Department of Information and Communications Engineering, Universitat Autonoma de Barcelona, Catalonia, Spain

    Guillermo Navarro-Arribas

  2. Consejo Superior de Investigaciones Científicas Campus de la UAB, Institut d'Investigació en Intel·ligència Artificial, Catalonia, Spain

    Vicenç Torra

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Casas-Roma, J., Herrera-Joancomartí, J., Torra, V. (2015). A Summary of \(k\)-Degree Anonymous Methods for Privacy-Preserving on Networks. In: Navarro-Arribas, G., Torra, V. (eds) Advanced Research in Data Privacy. Studies in Computational Intelligence, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-319-09885-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-09885-2_13

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  • Online ISBN: 978-3-319-09885-2

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