Cease at the Ultimate Goodness: Towards Efficient Website Fingerprinting Defense via Iterative Mutual Information Minimization

In response to growing online privacy threats, the Tor network offers essential protection against surveillance by routing traffic through a decentralized, encrypted infrastructure. However, Website Fingerprinting Attacks (WFA) present a formidable challenge to Tor's anonymity. This paper introduces FRUGAL, a traffic obfuscation method that leverages the mutual information (MI) reduction between website traffic and labels as an optimization goal, advancing a novel perspective for Website Fingerprinting Defense (WFD). By strategically injecting dummy packets at positions within website traffic that contribute most to cumulative MI reduction, FRUGAL achieves notable performance compared to state-of-the-art (SOTA) defense mechanisms. It effectively reduces attack success rates (ASR) across diverse attack models while maintaining minimal bandwidth overhead (BWO) and mitigating the impact of adversarial training. Extensive experiments validate the efficacy of FRUGAL across a comprehensive set of scenarios, including closed-world, open-world, and real-world simulation settings. For example, in the closed-world setting, FRUGAL reduces the ASR of the DF model to 2.68% with a 30% BWO, substantially outperforming previous SOTA defenses, such as Palette (11.54% with 87% BWO). When the BWO of FRUGAL is increased to a comparable level of 80%, the ASR further drops below 1%, demonstrating significant resilience by remaining low at 9.42% even after adversarial training, compared to 20.27% for Palette. This work not only introduces a fresh perspective on WFD research but also establishes FRUGAL as a robust and universal defense framework against WFA.