While the Internet has proven a powerful force for freedom, controlling the flow of online traffic is one of the most powerful methods used by 21st century totalitarian governments. By blocking or throttling areas of the Internet, a nation-state can slow or even entirely cut off the spread of information, or prevent the use of applications and tools it considers dangerous or threatening to its rule. But for obvious reasons, these governments involved do not advertise their activities, making it hard to detect these restrictions.
A new multi-institutional study led by University of Chicago Professor Nick Feamster will build new AI and data science tools to monitor and detect Internet censorship, develop new statistical techniques to identify censorship with greater levels of confidence, and ultimately create a “weather map” for certain types of nation-state interference and control of online information. The $1 million grant, funded by the U.S. Defense Advanced Research Projects Agency (DARPA), brings together researchers from the UChicago Data Science Institute (DSI), Princeton University, and SRI International.
“Our goal is to increase our levels of confidence about what is being censored, when censorship occurs, and where it is being censored,” said Feamster, Neubauer Professor of Computer Science and Director of Research for the Data Science Institute. “Our past work on measuring Internet censorship has developed tools to identify possible instances of censorship, but the measurements have some amount of uncertainty, and thus we need to develop more robust statistical techniques. We also aim to derive and provide real-time insights from that data—another significant data science challenge since the scope and scale of the Internet is so massive.”
The Internet is a global network of computers, and when governments want to interfere with or block information from entering their borders, they can do so at different “levels” of that network. By inserting firewalls, middleboxes, and other intermediary devices on the path into and out of their countries, authorities can interfere with traffic in a variety of ways, such as blocking or throttling websites or pages, such as news sites, online forums, or social media platforms. An authority could block a site or a page outright, or simply slow access to the point of being no longer usable.
Because Internet slowdowns and failures happen during the course of normal operations, distinguishing censorship from “normal” outages or slowdowns is a difficult task. But the massive amounts of data moving through the global network of the Internet contains subtle clues that this interference is happening, and because intentional slowdowns look different from “normal” ones, there is hope that AI can detect the difference. Feamster’s group will train new AI models and apply novel data science techniques to detect the “fingerprints” of these devices, giving internet watchdogs, policymakers, and citizen groups the ability to observe when and where censorship is happening.