Client Diversity Factor in HTTPS Webpage Fingerprinting

Abstract

Webpage fingerprinting methods infer the webpages visited in a traffic trace and are serious threats to the privacy of web users. Prior work evaluates webpage fingerprinting methods using traffic samples from a single client and does not consider the client diversity factor---webpages can be visited using different browsers, operating systems and devices. In this paper, we study the impact of client diversity on HTTPS webpage fingerprinting. First, we evaluate 5 prominent fingerprinting methods using traffic samples from 19 different clients. We show that the best performing methods overfit to the traffic patterns of a single client and do not generalize when they are evaluated using the samples from a different client (even if the clients use the same browser and operating system and only differ in device). Then, we investigate the traffic patterns of the clients and find differences in the HTTP messages generated, servers communicated and implementation of HTTP/2 across the clients. Finally, we show that the robustness of the methods can be increased by training them using the samples from a diverse set of clients. This study informs the community towards a realistic threat model for HTTPS webpage fingerprinting and presents an analysis of modern HTTPS traffic.