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Data and Structural k-Anonymity in Social Networks

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Privacy, Security, and Trust in KDD (PInKDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 5456))

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Abstract

The advent of social network sites in the last years seems to be a trend that will likely continue. What naive technology users may not realize is that the information they provide online is stored and may be used for various purposes. Researchers have pointed out for some time the privacy implications of massive data gathering, and effort has been made to protect the data from unauthorized disclosure. However, the data privacy research has mostly targeted traditional data models such as microdata. Recently, social network data has begun to be analyzed from a specific privacy perspective, one that considers, besides the attribute values that characterize the individual entities in the networks, their relationships with other entities. Our main contributions in this paper are a greedy algorithm for anonymizing a social network and a measure that quantifies the information loss in the anonymization process due to edge generalization.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Northern Kentucky University, Highland Heights, KY 41076, U.S.A.

    Alina Campan & Traian Marius Truta

Authors
  1. Alina Campan
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  2. Traian Marius Truta
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Editor information

Editors and Affiliations

  1. Yahoo! Research Barcelona, Avinguda Drive 177, 08018, Barcelona, Spain

    Francesco Bonchi

  2. Department of Computer Science and Communication, University of Insubria, 22100, Varese, Italy

    Elena Ferrari

  3. 311 Computer Science Building, 500W. 15th St., MO 65409, Rolla, USA

    Wei Jiang

  4. Department of Biomedical Informatics, Vanderbilt University, TN 37203, nashville, USA

    Bradley Malin

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© 2009 Springer-Verlag Berlin Heidelberg

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Campan, A., Truta, T.M. (2009). Data and Structural k-Anonymity in Social Networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds) Privacy, Security, and Trust in KDD. PInKDD 2008. Lecture Notes in Computer Science, vol 5456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01718-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-01718-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01717-9

  • Online ISBN: 978-3-642-01718-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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