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The Impact of Unlinkability on Adversarial Community Detection: Effects and Countermeasures

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Privacy Enhancing Technologies (PETS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 6205))

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Abstract

We consider the threat model of a mobile-adversary drawn from contemporary computer security literature, and explore the dynamics of community detection and hiding in this setting. Using a real-world social network, we examine the extent of network topology information an adversary is required to gather in order to accurately ascertain community membership information. We show that selective surveillance strategies can improve the adversary’s efficiency over random wiretapping. We then consider possible privacy preserving defenses; using anonymous communications helps, but not much; however, the use of counter-surveillance techniques can significantly reduce the adversary’s ability to learn community membership. Our analysis shows that even when using anonymous communications an adversary placing a selectively chosen 8% of the nodes of this network under surveillance (using key-logger probes) can de-anonymize the community membership of as much as 50% of the network. Uncovering all community information with targeted selection requires probing as much as 75% of the network. Finally, we show that a privacy conscious community can substantially disrupt community detection using only local knowledge even while facing up to the asymmetry of a completely knowledgeable mobile-adversary.

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Author information

Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, 1308 West Main Street, Urbana, IL 61801, USA

    Shishir Nagaraja

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  1. Shishir Nagaraja
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Editors and Affiliations

  1. Department of Computer Science, Purdue University, 305 North University Street, IN 47907, 2107, West Lafayete, USA

    Mikhail J. Atallah

  2. Department of Computer Science & Engineering, University of Minnesota, MN 55455, Minneapolis, USA

    Nicholas J. Hopper

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Nagaraja, S. (2010). The Impact of Unlinkability on Adversarial Community Detection: Effects and Countermeasures. In: Atallah, M.J., Hopper, N.J. (eds) Privacy Enhancing Technologies. PETS 2010. Lecture Notes in Computer Science, vol 6205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14527-8_15

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  • DOI: https://doi.org/10.1007/978-3-642-14527-8_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14526-1

  • Online ISBN: 978-3-642-14527-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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