Untraceable electronic mail, return addresses, and digital pseudonyms
Communications of the ACM
From a Trickle to a Flood: Active Attacks on Several Mix Types
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Probabilistic Treatment of MIXes to Hamper Traffic Analysis
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
DSSS-Based Flow Marking Technique for Invisible Traceback
SP '07 Proceedings of the 2007 IEEE Symposium on Security and Privacy
Markov models of internet traffic and a new hierarchical MMPP model
Computer Communications
Towards an information theoretic metric for anonymity
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
A fresh look at the generalised mix framework
PET'07 Proceedings of the 7th international conference on Privacy enhancing technologies
Two-sided statistical disclosure attack
PET'07 Proceedings of the 7th international conference on Privacy enhancing technologies
The traffic analysis of continuous-time mixes
PET'04 Proceedings of the 4th international conference on Privacy Enhancing Technologies
Timing analysis in low-latency mix networks: attacks and defenses
ESORICS'06 Proceedings of the 11th European conference on Research in Computer Security
Joint end-to-end loss-delay hidden Markov model for periodic UDP traffic over the Internet
IEEE Transactions on Signal Processing
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With the increasing requirement of privacy protection, various anonymity communication systems are designed and implemented. However, in the current communication infrastructure, traffic data can be gathered at moderate cost by adversary. Based on the traffic data, they can easily correlate the input links with output links by applying powerful traffic analysis techniques. In this paper, a Hidden Markov Model (HMM) approach is proposed to analyze one of the important anonymity systems, continuous mixes, which individually delays messages instead of processing batch messages. This approach consists of two parts, arrival traffic model and departure traffic model based on HMM, which capture the mean rates of the arrival and departure messages respectively. By using this approach to analyze anonymity of continuous mixes, a successful anonymity analysis can not be guaranteed, especially while the arrival traffic rate is greater than the departure traffic rate. In order to achieve better anonymity results, a new countermeasure is proposed, which inserts a minimum number of dummy traffic flows to ensure better anonymity of continuous mixes and protects users against various traffic analyses.