Papers by Tzu-Wen Lee

Automated Detection and Fingerprinting of Censorship Block Pages

This short paper, from IMC last year, presents a re-analysis of data collected by the OpenNet Initiative on overt censorship of the Web by a wide variety of countries. Overt means that when a webpage is censored, the user sees an error message which unambiguously informs them that it’s censored. (A censor can also act deniably, giving the user no proof that censorship is going on—the webpage just appears to be broken.) The goal of this reanalysis is to identify block pages (the error messages) automatically, distinguish them from normal pages, and distinguish them from each other—a new, unfamiliar format of block page may indicate a new piece of software is in use to do the censoring.

The chief finding is that block pages can be reliably distinguished from normal pages just by looking at their length: block pages are typically much shorter than normal. This is to be expected, seeing that they are just an error message. What’s interesting, though, is that this technique works better than techniques that look in more detail at the contents of the page. I’d have liked to see some discussion of what kinds of misidentification appear for each technique, but there probably wasn’t room for that. Length is not an effective tactic for distinguishing block pages from each other, but term frequency is (they don’t go into much detail about that).

One thing that’s really not clear is how they distinguish block pages from ordinary HTTP error pages. They mention that ordinary errors introduce significant noise in term-frequency clustering, but they don’t explain how they weeded them out. It might have been done manually; if so, that’s a major hole in the overall automated-ness of this process.