---
res:
  bibo_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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Timour
      foaf_name: Igamberdiev, Timour
      foaf_surname: Igamberdiev
  - foaf_Person:
      foaf_givenName: Thomas
      foaf_name: Arnold, Thomas
      foaf_surname: Arnold
  - foaf_Person:
      foaf_givenName: Ivan
      foaf_name: Habernal, Ivan
      foaf_surname: Habernal
      foaf_workInfoHomepage: http://www.librecat.org/personId=101881
  dct_date: 2022^xs_gYear
  dct_language: eng
  dct_publisher: International Committee on Computational Linguistics@
  dct_title: 'DP-Rewrite: Towards Reproducibility and Transparency in Differentially
    Private Text Rewriting@'
...