Traffic Confirmation Attacks Despite Noise
This work addresses privacy vulnerabilities in anonymous communication networks, showing attacks remain effective despite noise and padding, which is incremental but impactful for security practitioners.
The authors tackled traffic confirmation attacks on low-latency mix networks by developing a method using robust real-time binary hashes, achieving an 80% true positive match rate with less than 2% false positives when matching one flow out of 9000, and over 90% match rates against probabilistic padding schemes.
We propose a traffic confirmation attack on low-latency mix networks based on computing robust real-time binary hashes of network traffic flows. Firstly, we adapt the Coskun-Memon Algorithm to construct hashes that can withstand network impairments to allow fast matching of network flows. The resulting attack has a low startup cost and achieves a true positive match rate of 80% when matching one flow out of 9000 with less than 2% false positives, showing traffic confirmation attacks can be highly accurate even when only part of the network traffic flow is seen. Secondly, we attack probabilistic padding schemes achieving a match rate of over 90% from 9000 network traffic flows, showing advanced padding techniques are still vulnerable to traffic confirmation attacks.