While impressive, I don't think these results are actually that bad for privacy. 80% precision, for example, is useless when you're matching against tens of millions. It's much the same fallacy of the medical test for a disease that occurs in 1 out of 1000 people, and which has 99% accuracy -- but that still means a 90% false positive rate.<p>It reminds me of the claims of being able to identify, for example, the gender of an author with ~65% accuracy -- which is really actually completely unimpressive, as it's hardly better than guessing, and certainly not something you could rely on for any serious purpose.<p>The author mentions that topic is one way to help correlate beyond the results of the algorithm. But if I wrote "anonymous" posts in my area of expertise, you certainly would not need stylistic analysis to guess what my identity might be! There has never been privacy in this regard, I don't think.<p>Where privacy is needed most, I think, is exactly where this deanonymizing tool still isn't sufficient: talking about <i>unrelated topics</i>. A person should be free to express themselves under multiple names for different purposes, and there is no reason why an employer needs to know about a programmer's side hobby as a fiction writer if s/he doesn't want them to.<p>Finally, I do wonder how well these results correlate to the case where someone is <i>intentionally</i> operating under a different name. Matching one post by tech blogger A against blogger A is easy, because tech blogger A is making no attempt to write any differently or in any different context. However, what if tech-writer A ghost-wrote YA fiction on the side? Could you use these techniques to detect that the fiction was written by that blogger? It can't be ruled out without trying, but generalizing these results to that seems questionable.