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
Mixminion: Design of a Type III Anonymous Remailer Protocol
SP '03 Proceedings of the 2003 IEEE Symposium on Security and Privacy
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
Does additional information always reduce anonymity?
Proceedings of the 2007 ACM workshop on Privacy in electronic society
A framework for quantification of linkability within a privacy-enhancing identity management system
ETRICS'06 Proceedings of the 2006 international conference on Emerging Trends in Information and Communication Security
Practical traffic analysis: extending and resisting statistical disclosure
PET'04 Proceedings of the 4th 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
Vida: How to Use Bayesian Inference to De-anonymize Persistent Communications
PETS '09 Proceedings of the 9th International Symposium on Privacy Enhancing Technologies
Using Linkability Information to Attack Mix-Based Anonymity Services
PETS '09 Proceedings of the 9th International Symposium 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 three dimensional sender anonymity metric
International Journal of Security and Networks
ACM SIGCOMM Computer Communication Review
You cannot hide for long: de-anonymization of real-world dynamic behaviour
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
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This paper studies anonymity in a setting where individuals who communicate with each other over an anonymous channel are also members of a social network. In this setting the social network graph is known to the attacker. We propose a Bayesian method to combine multiple available sources of information and obtain an overall measure of anonymity. We study the effects of network size and find that in this case anonymity degrades when the network grows. We also consider adversaries with incomplete or erroneous information; characterize their knowledge of the social network by its quantity, quality and depth; and discuss the implications of these properties for anonymity.