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Managing and Mining Graph Data pp 421–453Cite as

A Survey of Privacy-Preservation of Graphs and Social Networks

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Part of the book series: Advances in Database Systems ((ADBS,volume 40))

Abstract

Social networks have received dramatic interest in research and development. In this chapter, we survey the very recent research development on privacypreserving publishing of graphs and social network data. We categorize the state-of-the-art anonymization methods on simple graphs in three main categories: K-anonymity based privacy preservation via edge modification, probabilistic privacy preservation via edge randomization, and privacy preservation via generalization. We then review anonymization methods on rich graphs. We finally discuss challenges and propose new research directions in this area.

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Author information

Authors and Affiliations

  1. University of North Carolina at Charlotte, Charlotte, NC, USA

    Xintao Wu & Xiaowei Ying

  2. Yahoo! Labs, Santa Clara, CA, USA

    Kun Liu

  3. Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Lei Chen

Authors
  1. Xintao Wu
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  2. Xiaowei Ying
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  3. Kun Liu
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  4. Lei Chen
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Corresponding author

Correspondence to Xintao Wu .

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Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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© 2010 Springer-Verlag US

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Wu, X., Ying, X., Liu, K., Chen, L. (2010). A Survey of Privacy-Preservation of Graphs and Social Networks. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_14

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_14

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  • Online ISBN: 978-1-4419-6045-0

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