ER-AE: Differentially Private Text Generation for Authorship Anonymization

Understand

Most of privacy protection studies for textual data focus on removing explicit sensitive identifiers.

• However, personal writing style, as a strong indicator of the authorship, is often neglected.
• Recent studies, such as SynTF, have shown promising results on privacy-preserving text mining.
• However, their anonymization algorithm can only output numeric term vectors which are difficult for the recipients to interpret.