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With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting

arXiv 2511.00487 ↗First posted 1 Nov 2025cs.CLcited by 0

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Abstract (arXiv)

Recent work in Differential Privacy with Natural Language Processing (DP NLP) has proposed numerous promising techniques in the form of text rewriting mechanisms. In the evaluation of these mechanisms, an often-ignored aspect is that of dataset size, or rather, the effect of dataset size on a mechanism's efficacy for utility and privacy preservation. In this work, we are the first to introduce this factor in the evaluation of DP text privatization, where we design utility and privacy tests on large-scale datasets with dynamic split sizes. We run these tests on datasets of varying size with up to one million texts, and we focus on quantifying the effect of increasing dataset size on the privacy-utility trade-off. Our findings reveal that dataset size plays an integral part in evaluating DP text rewriting mechanisms; additionally, these findings call for more rigorous evaluation procedures in DP NLP, as well as shed light on the future of DP NLP in practice and at scale.

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References · 15

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  5. 2023How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacycited by 150
  6. 2019Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbationscited by 115
  7. 2022The Limits of Word Level Differential Privacycited by 63
  8. 2023Locally Differentially Private Document Generation Using Zero Shot Promptingcited by 63
  9. 2022DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewritingcited by 28
  10. 2024A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-offcited by 11
  11. 2024Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Promptingcited by 11
  12. 20241-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacycited by 10
3 more
  1. 2025On the Impact of Noise in Differentially Private Text Rewritingcited by 8
  2. 2020Research Challenges in Designing Differentially Private Text Generation Mechanismscited by 7
  3. 2025Inspecting the Representation Manifold of Differentially-Private Textcited by 2

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