Most privacy protection studies for textual data focus on removing explicit sensitive identifiers. However, personal writing style, as a strong indicator of 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. We propose a novel text generation model with a two-set exponential mechanism for authorship anonymization. By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style. It does not assume any conditioned labels or paralleled text data for training. 


ER-AE: Differentially Private Text Generation for Authorship Anonymization



<i>**Session Chair: ** Minjoon Seo </i><br><br>

11D-Oral: NLP Applications

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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ER-AE: Differentially Private Text Generation for Authorship Anonymization

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