Countering Statistical Disclosure with Receiver-Bound Cover Traffic
Nayantara Mallesh1
and Matthew Wright1 
| (1) |
Department of Computer Science and Engineering, The University of Texas at Arlington, |
Abstract
Anonymous communications provides an important privacy service by keeping passive eavesdroppers from linking communicating
parties. However, using long-term statistical analysis of traffic sent to and from such a system, it is possible to link senders
with their receivers. Cover traffic is an effective, but somewhat limited, counter strategy against this attack. Earlier work
in this area proposes that privacy-sensitive users generate and send cover traffic to the system. However, users are not online
all the time and cannot be expected to send consistent levels of cover traffic, drastically reducing the impact of cover traffic.
We propose that the mix generate cover traffic that mimics the sending patterns of users in the system. This receiver-bound cover helps to make up for users that aren’t there, confusing the attacker. We show through simulation how this makes it difficult
for an attacker to discern cover from real traffic and perform attacks based on statistical analysis. Our results show that
receiver-bound cover substantially increases the time required for these attacks to succeed. When our approach is used in
combination with user-generated cover traffic, the attack takes a very long time to succeed.
Keywords privacy-enhancing technologies - cover traffic - anonymity
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