Statistical Privacy for Streaming Traffic

Machine learning empowers traffic-analysis attacks that breach users' privacy from their encrypted traffic. Recent advances in deep learning drastically escalate such threats. One prominent example demonstrated recently is a traffic-analysis attack against video streaming by using convolutional neural networks. In this paper, we explore the adaption of techniques previously used in the domains of adversarial machine learning and differential privacy to mitigate the machine-learning-powered analysis of streaming traffic.