Measuring Internet Traffic Manipulation

Traffic differentiation---giving better or worse performance to certain classes of Internet traffic---is a well-known but poorly understood traffic management policy. There is active discussion on whether and how ISPs should be allowed to differentiate Internet traffic, but little data about current practices to inform this discussion. Previous work attempted to address this problem for fixed line networks, but at the time of the publication of our work, there was no solution that works in the more challenging mobile environment. In terms of censorship measurement, despite the high perceived value and increasing severity of online information controls, a data-driven understanding of the phenomenon has remained elusive. In this report, we first present the design, implementation and evaluation of Differentiation Detector, the first system and mobile app for identifying traffic differentiation for arbitrary applications in the mobile environment. Next we introduce Information Controls Lab (ICLab)---a project focused on collecting and analyzing reliable information controls data on the Internet at scale---by comparing it with another design point in the space of Internet censorship measurement with particular emphasis on how they address the challenges of locating vantage points, choosing content to test, and analyzing results. We discuss the trade offs of decisions made by each platform and show how the resulting data provides complementary views of global censorship, as well as the lessons learned and open challenges discovered through our experiences. | 52 pages