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International Conference on Modeling Decisions for Artificial Intelligence

MDAI 2014: Modeling Decisions for Artificial Intelligence pp 204–216Cite as

Privacy-Preserving on Graphs Using Randomization and Edge-Relevance

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8825))

Abstract

The problem of anonymization on graphs and the utility of the released data are considered in this paper. Although there are some anonymization methods for graphs, most of them cannot be applied on medium or large networks due to their complexity. Nevertheless, random-based methods are able to work with medium or large networks while fulfilling the desired privacy level. In this paper, we devise a simple and efficient algorithm for randomization on graphs. Our algorithm considers the edge’s relevance, preserving the most important edges of the graph, in order to improve the data utility and reduce the information loss on anonymous data. We apply our algorithm to different real datasets and demonstrate their efficiency and practical utility.

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Author information

Authors and Affiliations

  1. Universitat Oberta de Catalunya, Barcelona, Spain

    Jordi Casas-Roma

Authors
  1. Jordi Casas-Roma
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Editor information

Editors and Affiliations

  1. CSIC, Consejo Superior de Investigaciones Cientificas, 08193, Bellaterra, Spain

    Vicenç Torra

  2. Toho Gakuen, 186-0004, Tokyo, Japan

    Yasuo Narukawa

  3. Faculty of Engineering, Information and Systems, University of Tsukuba, 305-8573, Tsukuba, Ibaraki, Japan

    Yasunori Endo

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Casas-Roma, J. (2014). Privacy-Preserving on Graphs Using Randomization and Edge-Relevance. In: Torra, V., Narukawa, Y., Endo, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2014. Lecture Notes in Computer Science(), vol 8825. Springer, Cham. https://doi.org/10.1007/978-3-319-12054-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-12054-6_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12053-9

  • Online ISBN: 978-3-319-12054-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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