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Unmixing Mix Traffic

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Privacy Enhancing Technologies (PET 2005)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 3856))

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Abstract

We apply blind source separation techniques from statistical signal processing to separate the traffic in a mix network. Our experiments show that this attack is effective and scalable. By combining the flow separation method and frequency spectrum matching method, a passive attacker can get the traffic map of the mix network. We use a non-trivial network to show that the combined attack works. The experiments also show that multicast traffic can be dangerous for anonymity networks.

This work is supported in part by the Texas Information Technology and Telecommunication Task Force.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Texas A&M University, College Station, TX, 77843-3112, USA

    Ye Zhu & Riccardo Bettati

Authors
  1. Ye Zhu
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  2. Riccardo Bettati
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Editor information

Editors and Affiliations

  1. Microsoft Research, Cambridge, UK

    George Danezis

  2. Artificial Intelligence Center, SRI International, USA

    David Martin

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhu, Y., Bettati, R. (2006). Unmixing Mix Traffic. In: Danezis, G., Martin, D. (eds) Privacy Enhancing Technologies. PET 2005. Lecture Notes in Computer Science, vol 3856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11767831_8

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  • DOI: https://doi.org/10.1007/11767831_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34745-3

  • Online ISBN: 978-3-540-34746-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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