ER-AE: Differentially-private Text Generation for Authorship Anonymization
Table 1: Results for each evaluation metric on both datasets. ↑ indicates the higher the better. ↓ indicates the lower the better.
['Model', 'Yelp (100-author) USE ↑', 'Yelp (100-author) Authorship ↓', 'Yelp (100-author) Stylometric↑', 'Conferences’ Dataset USE↑', 'Conferences’ Dataset Stylometric↑']
With a low utility (USE) score around 0.2 for both the Yelp review and the peer review datasets, SynTF, and Random-R generate grammatically incorrect text and completely change the meaning of the original one. In contrast, ER-AE without semantic augmentation through REINFORCE training, denoted as AE-DP, achieves a much higher utility score of around 0.57 and 0.54. The full model ER-AE achieves the highest utility score of 0.79 for Yelp reviews and 0.74 for peer reviews.