DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting

Timour Igamberdiev, Thomas Arnold, Ivan Habernal


Abstract
Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent.
Anthology ID:
2022.coling-1.258
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2927–2933
Language:
URL:
https://aclanthology.org/2022.coling-1.258
DOI:
Bibkey:
Cite (ACL):
Timour Igamberdiev, Thomas Arnold, and Ivan Habernal. 2022. DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2927–2933, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting (Igamberdiev et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.258.pdf
Code
 trusthlt/dp-rewrite
Data
SNIPS