Unmixing mix traffic

  • Authors:
  • Ye Zhu;Riccardo Bettati

  • Affiliations:
  • Department of Computer Science, Texas A&M University, College Station, TX;Department of Computer Science, Texas A&M University, College Station, TX

  • Venue:
  • PET'05 Proceedings of the 5th international conference on Privacy Enhancing Technologies
  • Year:
  • 2005

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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.