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Anonymisation of Heterogeneous Graphs with Multiple Edge Types

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Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

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Abstract

Anonymisation is a strategy often employed when sharing and exchanging data that contains personal and sensitive information, to avoid possible record identification or inference. Besides the actual attributes contained within a dataset, also certain other aspects might reveal information on the data subjects. One example of this is the structure within a graph, i.e. the connection between nodes. These might allow to re-identify a specific person, e.g. by knowledge of the number of connections for some individuals within the dataset.

Thus, anonymisation of the structure is an important aspect of achieving privacy. In this paper, we therefore present an algorithm that extends upon the current state of the art by considering multiple types of connections (relations) between nodes.

SBA Research (SBA-K1) is a COMET Centre within the framework of COMET - Competence Centers for Excellent Technologies Programme and funded by BMK, BMDW, and the federal state of Vienna; COMET is managed by FFG. This work is supported by FFG under Grant No. 871299 (project KnoP-2D).

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Notes

  1. 1.

    https://www.w3.org/RDF/.

  2. 2.

    https://github.com/sbaresearch/graph-anonymisation.

  3. 3.

    http://www.foaf-project.org/.

  4. 4.

    Note that apart from reducing computational complexity, it is logical to target individuals, since it is the most common setting when facing ananonymisation task.

  5. 5.

    Node value anonymisation, if necessary, is a pre-requisite step and not covered by our structure anonymisation method.

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Authors and Affiliations

  1. SBA Research, Vienna, Austria

    Guillermo Alamán Requena, Rudolf Mayer & Andreas Ekelhart

Authors
  1. Guillermo Alamán Requena
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  2. Rudolf Mayer
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  3. Andreas Ekelhart
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Corresponding author

Correspondence to Rudolf Mayer .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Alamán Requena, G., Mayer, R., Ekelhart, A. (2022). Anonymisation of Heterogeneous Graphs with Multiple Edge Types. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-12423-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12422-8

  • Online ISBN: 978-3-031-12423-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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