  Two interlocking research questions of growing interest and importance in
privacy research are Authorship Attribution (AA) and Authorship Obfuscation
(AO). Given an artifact, especially a text t in question, an AA solution aims
to accurately attribute t to its true author out of many candidate authors
while an AO solution aims to modify t to hide its true authorship.
Traditionally, the notion of authorship and its accompanying privacy concern is
only toward human authors. However, in recent years, due to the explosive
advancements in Neural Text Generation (NTG) techniques in NLP, capable of
synthesizing human-quality open-ended texts (so-called "neural texts"), one has
to now consider authorships by humans, machines, or their combination. Due to
the implications and potential threats of neural texts when used maliciously,
it has become critical to understand the limitations of traditional AA/AO
solutions and develop novel AA/AO solutions in dealing with neural texts. In
this survey, therefore, we make a comprehensive review of recent literature on
the attribution and obfuscation of neural text authorship from a Data Mining
perspective, and share our view on their limitations and promising research
directions.
