Untraceable electronic mail, return addresses, and digital pseudonyms
Communications of the ACM
Limits of Anonymity in Open Environments
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Mixminion: Design of a Type III Anonymous Remailer Protocol
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Heartbeat traffic to counter (n-1) attacks: red-green-black mixes
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Proceedings of the second ACM workshop on Digital identity management
Tor: the second-generation onion router
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Measuring Anonymity: The Disclosure Attack
IEEE Security and Privacy
Revisiting a combinatorial approach toward measuring anonymity
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Towards an information theoretic metric for anonymity
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
Measuring anonymity with relative entropy
FAST'06 Proceedings of the 4th international conference on Formal aspects in security and trust
Two-sided statistical disclosure attack
PET'07 Proceedings of the 7th international conference on Privacy enhancing technologies
Sampled traffic analysis by internet-exchange-level adversaries
PET'07 Proceedings of the 7th international conference on Privacy enhancing technologies
Statistical disclosure or intersection attacks on anonymity systems
IH'04 Proceedings of the 6th international conference on Information Hiding
Reasoning about the anonymity provided by pool mixes that generate dummy traffic
IH'04 Proceedings of the 6th international conference on Information Hiding
The hitting set attack on anonymity protocols
IH'04 Proceedings of the 6th international conference on Information Hiding
Practical traffic analysis: extending and resisting statistical disclosure
PET'04 Proceedings of the 4th international conference on Privacy Enhancing Technologies
Measuring anonymity in a non-adaptive, real-time system
PET'04 Proceedings of the 4th international conference on Privacy Enhancing Technologies
On blending attacks for mixes with memory
IH'05 Proceedings of the 7th international conference on Information Hiding
Impact of network topology on anonymity and overhead in low-latency anonymity networks
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
Drac: an architecture for anonymous low-volume communications
PETS'10 Proceedings of the 10th international conference on Privacy enhancing technologies
A practical complexity-theoretic analysis of mix systems
ESORICS'11 Proceedings of the 16th European conference on Research in computer security
Quantitative information flow, with a view
ESORICS'11 Proceedings of the 16th European conference on Research in computer security
Statistical measurement of information leakage
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
The dangers of composing anonymous channels
IH'12 Proceedings of the 14th international conference on Information Hiding
Review: An overview of anonymity technology usage
Computer Communications
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This work casts the traffic analysis of anonymity systems, and in particular mix networks, in the context of Bayesian inference. A generative probabilistic model of mix network architectures is presented, that incorporates a number of attack techniques in the traffic analysis literature. We use the model to build an Markov Chain Monte Carlo inference engine, that calculates the probabilities of who is talking to whom given an observation of network traces. We provide a thorough evaluation of its correctness and performance, and confirm that mix networks with realistic parameters are secure. This approach enables us to apply established information theoretic anonymity metrics on complex mix networks, and extract information from anonymised traffic traces optimally.