SmartSwitch: Efficient Traffic Obfuscation Against Stream Fingerprinting

In stream fingerprinting, an attacker can compromise user privacy by leveraging side-channel information (e.g., packet size) of encrypted traffic in streaming services. By taking advantages of machine learning, especially neural networks, an adversary can reveal which YouTube video a victim watches with extremely high accuracy. While effective defense methods have been proposed, extremely high bandwidth overheads are needed. In other words, building an effective defense with low overheads remains unknown. In this paper, we propose a new defense mechanism, referred to asSmartSwitch, to address this open problem. Our defense intelligently switches the noise level on different packets such that the defense remains effective but minimizes overheads. Specifically, our method produces higher noises to obfuscate the sizes of more significant packets. To identify which packets are more significant, we formulate it as a feature selection problem and investigate several feature selection methods over high-dimensional data. Our experimental results derived from a large-scale dataset demonstrate that our proposed defense is highly effective against stream fingerprinting built upon Convolutional Neural Networks. Specifically, an adversary can infer which YouTube video a user watches with only 1% accuracy (same as random guess) even if the adversary retrains neural networks with obfuscated traffic. Compared to the state-of-the-art defense, our mechanism can save nearly 40% of bandwidth overheads.