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Statistical Disclosure or Intersection Attacks on Anonymity Systems

  • George Danezis
  • Andrei Serjantov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3200)

Abstract

In this paper we look at the information an attacker can extract using a statistical disclosure attack. We provide analytical results about the anonymity of users when they repeatedly send messages through a threshold mix following the model of Kesdogan, Agrawal and Penz [7] and through a pool mix. We then present a statistical disclosure attack that can be used to attack models of anonymous communication networks based on pool mixes. Careful approximations make the attack computationally efficient. Such models are potentially better suited to derive results that could apply to the security of real anonymous communication networks.

Keywords

Statistical Disclosure Covert Channel Anonymous Communication Careful Approximation Abstract Threshold 
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 2004

Authors and Affiliations

  • George Danezis
    • 1
  • Andrei Serjantov
    • 1
  1. 1.Computer Laboratory, William Gates BuildingUniversity of CambridgeCambridgeUnited Kingdom

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