  Text-based analysis methods allow to reveal privacy relevant author
attributes such as gender, age and identify of the text's author. Such methods
can compromise the privacy of an anonymous author even when the author tries to
remove privacy sensitive content. In this paper, we propose an automatic
method, called Adversarial Author Attribute Anonymity Neural Translation
($A^4NT$), to combat such text-based adversaries. We combine
sequence-to-sequence language models used in machine translation and generative
adversarial networks to obfuscate author attributes. Unlike machine translation
techniques which need paired data, our method can be trained on unpaired
corpora of text containing different authors. Importantly, we propose and
evaluate techniques to impose constraints on our $A^4NT$ to preserve the
semantics of the input text. $A^4NT$ learns to make minimal changes to the
input text to successfully fool author attribute classifiers, while aiming to
maintain the meaning of the input. We show through experiments on two different
datasets and three settings that our proposed method is effective in fooling
the author attribute classifiers and thereby improving the anonymity of
authors.
