  We address the problem of how to "obfuscate" texts by removing stylistic
clues which can identify authorship, whilst preserving (as much as possible)
the content of the text. In this paper we combine ideas from "generalised
differential privacy" and machine learning techniques for text processing to
model privacy for text documents. We define a privacy mechanism that operates
at the level of text documents represented as "bags-of-words" - these
representations are typical in machine learning and contain sufficient
information to carry out many kinds of classification tasks including topic
identification and authorship attribution (of the original documents). We show
that our mechanism satisfies privacy with respect to a metric for semantic
similarity, thereby providing a balance between utility, defined by the
semantic content of texts, with the obfuscation of stylistic clues. We
demonstrate our implementation on a "fan fiction" dataset, confirming that it
is indeed possible to disguise writing style effectively whilst preserving
enough information and variation for accurate content classification tasks.
