Real-Time Website Fingerprinting Defense via Traffic Cluster Anonymization

Meng Shen, Kexin Ji, Jinhe Wu, Qi Li, Xiangdong Kong, Ke Xu, Liehuang Zhu

Abstract: Website Fingerprinting (WF) attacks significantly threaten user privacy in anonymity networks such as Tor. While numerous defenses have been proposed, they are unable to efficiently defend against recent deep learning based WF attacks. In this paper, we propose Palette, a novel and practical WF defense that utilizes traffic cluster anonymization to protect live Tor traffic. By clustering websites with high similarity in traffic patterns and regulating them into a well-designed uniform pattern for a cluster (i.e., a group of similar websites), Palette prevents attackers from distinguishing between these similar websites within the cluster and further provides a strong anonymity guarantee. Comprehensive evaluations with public real-world datasets show that Palette is superior to the existing defenses, greatly reducing the accuracy of the state-of-the-art (SOTA) WF attacks with acceptable overheads. Furthermore, we implement Palette as a Pluggable Transport in the Tor network. The experiment results demonstrate that, on average, Palette effectively reduces the accuracy of the SOTA WF attacks by 73.60%, which improves the existing defenses by 33.50%-43.47%.