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On Flow Correlation Attacks and Countermeasures in Mix Networks

  • Ye Zhu
  • Xinwen Fu
  • Bryan Graham
  • Riccardo Bettati
  • Wei Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3424)

Abstract

In this paper, we address issues related to flow correlation attacks and the corresponding countermeasures in mix networks. Mixes have been used in many anonymous communication systems and are supposed to provide countermeasures that can defeat various traffic analysis attacks. In this paper, we focus on a particular class of traffic analysis attack, flow correlation attacks, by which an adversary attempts to analyze the network traffic and correlate the traffic of a flow over an input link at a mix with that over an output link of the same mix. Two classes of correlation methods are considered, namely time-domain methods and frequency-domain methods. Based on our threat model and known strategies in existing mix networks, we perform extensive experiments to analyze the performance of mixes. We find that a mix with any known batching strategy may fail against flow correlation attacks in the sense that for a given flow over an input link, the adversary can correctly determine which output link is used by the same flow. We also investigated methods that can effectively counter the flow correlation attack and other timing attacks. The empirical results provided in this paper give an indication to designers of Mix networks about appropriate configurations and alternative mechanisms to be used to counter flow correlation attacks.

Keywords

Mutual Information Output Link Pattern Vector Analysis Attack Input Link 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ye Zhu
    • 1
  • Xinwen Fu
    • 1
  • Bryan Graham
    • 1
  • Riccardo Bettati
    • 1
  • Wei Zhao
    • 1
  1. 1.Department of Computer ScienceTexas A&M UniversityCollege StationUSA

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