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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.
Shehar Bano is a postdoctoral researcher at University College London. Her research interests centre on networked systems, particularly in the context of security and measurement. Currently, she is working on:
the DECODE platform—a distributed, privacy aware, and trusted architecture based on blockchain technology for decentralized data governance and identity management,
characterizing churn in the availability of IP addresses and Internet services over time and across different geographic locations in Internet-wide scans,and
understanding emerging forms of censorship such as conspiracy theories and propaganda in online media.
She completed her Ph.D. from Cambridge under the supervision of Prof. Jon Crowcroft (and co-supervised by Dr. Steven Murdoch, Prof. Vern Paxson, and Prof. Ross Anderson) where she was an Honorary Cambridge Trust Scholar, and was awarded the Mary Bradburn Scholarship by the British Federation of Women Graduates for her research work. Her thesis contributes novel measurement methodologies to identify instances of Internet censorship, and large-scale characterizations of such practices to shed light on how it’s done, how it can be stopped, what its effects are, and the evolving shape of the ecosystem of government/policy-based censorship. Previously she worked on Intrusion Detection Systems, and wrote an open-source software for botnet detection. Her work has been published in the Network and Distributed System Security Symposium, the ACM Internet Measurement Conference, the Symposium on Privacy Enhancing Technologies, and other well-respected venues.