We’re picking back up with a paper that’s brand new—so new that it exists only as an arXiv preprint and I don’t know if it is planned to be published anywhere. It probably hasn’t gone through formal peer review yet.
Wright and colleagues observe that because Tor is commonly used to evade censorship, changes in the number of people using Tor from any given country are a signal of a change in the censorship régime in that country. This isn’t a new idea: the Tor project itself has been doing something similar since 2011. What this paper does is present an improved algorithm for detecting such changes. It uses PCA to compare the time series of Tor active users across countries. The idea is that if there’s a change in Tor usage worldwide, that probably doesn’t indicate censorship, but a change in just a few countries is suspicious. To model this using PCA, they tune the number of
principal components so that the projected data matrix is well-divided into what they call
anomalous subspaces; large components in the anomalous subspace for any data vector indicate that that country at that time is not well-predicted by all the other countries, i.e. something fishy is going on.
They show that their algorithm can pick out previously known cases where a change in Tor usage is correlated with a change in censorship, and that its top ten
most anomalous countries are mostly the countries one would expect to be suspicious by this metric—but also a couple that nobody had previously suspected, which they highlight as a matter needing further attention.
PCA used as an anomaly detector is a new idea on me. It seems like they could be extracting more information from it than they are. The graphs in this paper show what’s probably a global jump in Tor usage in mid-2013; this has a clear explanation, and they show that their detector ignores it (as it’s supposed to), but can they make their detector call it out separately from country-specific events? PCA should be able to do that. Similarly, it seems quite probable that the ongoing revolutions and wars in the Levant and North Africa are causing correlated changes to degree of censorship region-wide; PCA should be able to pull that out as a separate explanatory variable. These would both involve taking a closer look at the
normal subspace and what each of its dimensions mean.
It also seems to me that a bit of preprocessing, using standard time series decomposition techniques, would
clean up the analysis and make its results easier to interpret. There’s not one word about that possibility in the paper, which seems like a major omission; decomposition is the first thing that anyone who knows anything about time series analysis would think of. In this case, I think seasonal variation should definitely be factored out, and removing linear per-country trends might also helpful.