WFDefProxy: Modularly Implementing and Empirically Evaluating Website Fingerprinting Defenses

TL;DR

This paper presents WFDefProxy, a platform for implementing and empirically evaluating website fingerprinting defenses on Tor, demonstrating that simulation results align with real-world performance and revealing differences in overhead and effectiveness.

Contribution

It provides the first full implementation and real-world evaluation of three WF defenses on Tor, comparing simulation and actual deployment results.

Findings

Simulation accurately predicts defense strength against attacks.

Random-WT is largely ineffective in both simulation and real-world.

Implementation reveals differences in overhead compared to simulation.

Abstract

Tor, an onion-routing anonymity network, has been shown to be vulnerable to Website Fingerprinting (WF), which de-anonymizes web browsing by analyzing the unique characteristics of the encrypted network traffic. Although many defenses have been proposed, few have been implemented and tested in the real world; others were only simulated. Due to its synthetic nature, simulation may fail to capture the real performance of these defenses. To figure out how these defenses perform in the real world, we propose WFDefProxy, a general platform for WF defense implementation on Tor using pluggable transports. We create the first full implementation of three WF defenses: FRONT, Tamaraw and Random-WT. We evaluate each defense in both simulation and implementation to compare their results, and we find that simulation correctly captures the strength of each defense against attacks. In addition, we confirm that Random-WT is not effective in both simulation and implementation, reducing the strongest attacker's accuracy by only 7%. We also found a minor difference in overhead between simulation and implementation. We analyze how this may be due to assumptions made in simulation regarding packet delays and queuing, or the soft stop condition we implemented in WFDefProxy to detect the end of a page load. The implementation of FRONT cost about 23% more data overhead than simulation, while the implementation of Tamaraw cost about 28% - 45% less data overhead. In addition, the implementation of Tamaraw incurred only 21% time overhead, compared to 51% - 242% estimated by simulation in previous work.