Defending Against Authorship Identification Attacks
Haining Wang
Authorship identification has proven unsettlingly effective in inferring the
identity of the author of an unsigned document, even when sensitive personal
information has been carefully omitted. In the digital era, individuals leave a
lasting digital footprint through their written content, whether it is posted
on social media, stored on their employer's computers, or located elsewhere.
When individuals need to communicate publicly yet wish to remain anonymous,
there is little available to protect them from unwanted authorship
identification. This unprecedented threat to privacy is evident in scenarios
such as whistle-blowing. Proposed defenses against authorship identification
attacks primarily aim to obfuscate one's writing style, thereby making it
unlinkable to their pre-existing writing, while concurrently preserving the
original meaning and grammatical integrity. The presented work offers a
comprehensive review of the advancements in this research area spanning over
the past two decades and beyond. It emphasizes the methodological frameworks of
modification and generation-based strategies devised to evade authorship
identification attacks, highlighting joint efforts from the differential
privacy community. Limitations of current research are discussed, with a
spotlight on open challenges and potential research avenues.