Characterization of Internet Censorship from Multiple Perspectives
Censorship of online communications threatens principles of openness and freedom of information on which the Internet was founded. In the interest of transparency and accountability, and more broadly to develop scientific rigour in the field, we need methodologies to measure and characterize Internet censorship. Such studies will not only help users make informed choices about information access, but also illuminate entities involved in or affected by censorship; informing the development of policy and enquiries into the ethics and legality of such practices. However, many issues around Internet censorship remain poorly understood because of the inherently adversarial and opaque landscape in which it operates. As details about mechanisms and targets of censorship are usually undisclosed, it is hard to define exactly what comprises censorship, and how it operates in different contexts.
My research aims to help fill this gap by developing methodologies to derive censorship ground truth using active and passive data analysis techniques, which I apply to real-world datasets to uncover entities involved in censorship, the targets of censorship, and the effects of such practices on different stakeholders. In this talk, I will provide an overview of my work on Internet censorship from multiple perspectives: (i) measurement of the Great Firewall of China that shows that inference of the censor’s traffic analysis model can enable systematic identification of evasion opportunities that users can exploit to access restricted content, (ii) analysis of network logs collected at an Internet Service Provider (ISP) in Pakistan over a period of escalating censorship to study how censorship affects users’ browsing habits with respect to circumvention, and its economic effects on content providers and ISPs, and (iii) investigation of differential treatment -- an emerging class of censorship where websites (rather than the government) block requests of users they don’t like -- in the context of Tor anonymity network and users of adblocking software.