Use fewer instances of the letter "i": toward writing style anonymization
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Semi-random subspace method for writeprint identification
Neurocomputing
Exploiting innocuous activity for correlating users across sites
Proceedings of the 22nd international conference on World Wide Web
Detecting multiple aliases in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
k-subscription: privacy-preserving microblogging browsing through obfuscation
Proceedings of the 29th Annual Computer Security Applications Conference
Proceedings of the 2013 workshop on New security paradigms workshop
Proceedings of the first ACM conference on Learning @ scale conference
Hi-index | 0.00 |
We study techniques for identifying an anonymous author via linguistic stylometry, i.e., comparing the writing style against a corpus of texts of known authorship. We experimentally demonstrate the effectiveness of our techniques with as many as 100,000 candidate authors. Given the increasing availability of writing samples online, our result has serious implications for anonymity and free speech--an anonymous blogger or whistleblower may be unmasked unless they take steps to obfuscate their writing style. While there is a huge body of literature on authorship recognition based on writing style, almost none of it has studied corpora of more than a few hundred authors. The problem becomes qualitatively different at a large scale, as we show, and techniques from prior work fail to scale, both in terms of accuracy and performance. We study a variety of classifiers, both "lazy" and "eager," and show how to handle the huge number of classes. We also develop novel techniques for confidence estimation of classifier outputs. Finally, we demonstrate stylometric authorship recognition on texts written in different contexts. In over 20% of cases, our classifiers can correctly identify an anonymous author given a corpus of texts from 100,000 authors; in about 35% of cases the correct author is one of the top 20 guesses. If we allow the classifier the option of not making a guess, via confidence estimation we are able to increase the precision of the top guess from 20% to over 80% with only a halving of recall.