Understanding the effects of real-world behavior in statistical disclosure attacks

@article{Oya2014UnderstandingTE,
  title={Understanding the effects of real-world behavior in statistical disclosure attacks},
  author={Simon Oya and Carmela Troncoso and Fernando P{\'e}rez-Gonz{\'a}lez},
  journal={2014 IEEE International Workshop on Information Forensics and Security (WIFS)},
  year={2014},
  pages={72-77}
}
High-latency anonymous communication systems prevent passive eavesdroppers from inferring communicating partners with certainty. However, disclosure attacks allow an adversary to recover users' behavioral profiles when communications are persistent. Understanding how the system parameters affect the privacy of the users against such attacks is crucial. Earlier work in the area analyzes the performance of disclosure attacks in controlled scenarios, where a certain model about the users' behavior… 

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References

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TLDR
The LSDA is presented, a novel disclosure attack based on the Maximum Likelihood (ML) approach, in which user profiles are estimated solving a Least Squares problem, and it is verified through simulation that the predictors for the error closely model reality, and that the LSDA recovers users' profiles with greater accuracy than its predecessors.
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TLDR
A framework to compare between the members of the statistical disclosure attack family is proposed and it is confirmed that LSDA outperforms the SDA family when the adversary has enough observations of the system.
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TLDR
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TLDR
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An improvement over the previously known disclosure attack is presented that allows, using statistical methods, to effectively deanonymize users of a mix system. Furthermore the statistical
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