Internet users in many countries around the world are subject to various forms of censorship and information control. Despite its widespread nature, however, measuring Internet censorship on a global scale has remained an elusive goal. Internet censorship is known to vary across time and within regions (and Internet Service Providers) within a country. To capture these complex dynamics, Internet censorship measurement must be both continuous and distributed across a large number of vantage points. To date, gathering such information has required recruiting volunteers to perform measurements from within countries of interest; this approach does not permit collection of continuous measurements, and it also does not permit collection from a large number of measurement locations; it may also put the people performing the measurements at risk. Over the past four years, we have developed a collection of measurement techniques to surmount the limitations of these conventional approaches. In this talk, I will describe three such techniques: (1) Encore, a tool that performs cross-origin requests to measure Web filtering; (2) Augur, a tool that exploits side-channel information in the Internet Protocol (IP) to measure filtering using network-level access control lists; and (3) a tool to measure DNS filtering using queries through open DNS resolvers. These three tools allow us—for the first time ever—to characterize Internet censorship continuously, from hundreds of countries around the world, at different layers of the network protocol stack. each of these techniques involves both technical and ethical challenges. I will describe some of the challenges that we faced in designing and implementing these tools, how we tackled these challenges, our experiences with measurements to date, and our plans for the future. Long term, our goal is to collaborate with social scientists to bring data to bear on a wide variety of questions concerning Internet censorship and information control; I will conclude with an appeal to cross-disciplinary work in this area and some ideas for how computer scientists and social scientists might work together on some of these pressing questions going forward.
This research is in collaboration with Sam Burnett, Roya Ensafi, Paul Pearce, Ben Jones, Frank Li, and Vern Paxson.
