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Sampling and Merging for Graph Anonymization

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Modeling Decisions for Artificial Intelligence (MDAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9880))

Abstract

We propose a method for network anonymization that consists on sampling a subset of vertices and merging its neighborhoods in the network. In such a way, by publishing the merged graph of the network together with the sampled vertices and their locally anonymized neighborhoods, we obtain a complete anonymized picture of the network. We prove that the anonymization of the merged graph incurs in lower information loss, hence, it has more utility than the direct anonymization of the graph. It also yields an improvement on the quality of the anonymization of the local neighbors of a given subset of vertices.

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Acknowledgements

Support by Spanish MCYT under project SmartGlacis TIN2014-57364-C2-1-R is acknowledged.

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Authors and Affiliations

  1. Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain

    Julián Salas

Authors
  1. Julián Salas
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Correspondence to Julián Salas .

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Editors and Affiliations

  1. University of Kövde , Skövde, Sweden

    Vicenç Torra

  2. Toho Gakuen , Kunitachi, Tokyo, Japan

    Yasuo Narukawa

  3. Enginyeria de la Informacio I de les Com, Univ Autonoma de Barcelona, Bellaterra, Spain

    Guillermo Navarro-Arribas

  4. Universitat d'Andorra , Sant Julià de Lòria, Andorra

    Cristina Yañez

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Salas, J. (2016). Sampling and Merging for Graph Anonymization. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-45656-0_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45655-3

  • Online ISBN: 978-3-319-45656-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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