  Users posting online expect to remain anonymous unless they have logged in,
which is often needed for them to be able to discuss freely on various topics.
Preserving the anonymity of a text's writer can be also important in some other
contexts, e.g., in the case of witness protection or anonymity programs.
However, each person has his/her own style of writing, which can be analyzed
using stylometry, and as a result, the true identity of the author of a piece
of text can be revealed even if s/he has tried to hide it. Thus, it could be
helpful to design automatic tools that can help a person obfuscate his/her
identity when writing text. In particular, here we propose an approach that
changes the text, so that it is pushed towards average values for some general
stylometric characteristics, thus making the use of these characteristics less
discriminative. The approach consists of three main steps: first, we calculate
the values for some popular stylometric metrics that can indicate authorship;
then we apply various transformations to the text, so that these metrics are
adjusted towards the average level, while preserving the semantics and the
soundness of the text; and finally, we add random noise. This approach turned
out to be very efficient, and yielded the best performance on the Author
Obfuscation task at the PAN-2016 competition.
