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

arXiv: https://arxiv.org/abs/2208.10400 · HTML: https://mlsanity.com/paper/2208.10400/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [The Limits of Word Level Differential Privacy](https://mlsanity.com/paper/2205.02130/) (2022)
- [ER-AE: Differentially Private Text Generation for Authorship Anonymization](https://mlsanity.com/paper/1907.08736/) (2019)
- [SoK: Differential Privacies](https://mlsanity.com/paper/1906.01337/) (2019)
- [ADePT: Auto-encoder based Differentially Private Text Transformation](https://mlsanity.com/paper/2102.01502/) (2021)
- [When differential privacy meets NLP: The devil is in the detail](https://mlsanity.com/paper/2109.03175/) (2021)
- [Privacy-Preserving Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2102.09604/) (2021)
- [How reparametrization trick broke differentially-private text representation learning](https://mlsanity.com/paper/2202.12138/) (2022)

## Built on by (most cited first, 28 of 28)

- [DP-BART for Privatized Text Rewriting under Local Differential Privacy](https://mlsanity.com/paper/2302.07636/) (2023)
- [Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges](https://mlsanity.com/paper/2510.23883/) (2025)
- [Grounding Foundation Models through Federated Transfer Learning: A General Framework](https://mlsanity.com/paper/2311.17431/) (2023)
- [Differentially Private Natural Language Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2301.09112/) (2023)
- [A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage](https://mlsanity.com/paper/2504.21035/) (2025)
- [A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking The Privacy-Utility Trade-off](https://mlsanity.com/paper/2404.03324/) (2024)
- [Seven Security Challenges in Cross-domain Multi-agent LLM Systems](https://mlsanity.com/paper/2505.23847/) (2025)
- [KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server](https://mlsanity.com/paper/2410.05725/) (2024)
- [Privacy-Preserving Models for Legal Natural Language Processing](https://mlsanity.com/paper/2211.02956/) (2022)
- [Private Synthetic Text Generation with Diffusion Models](https://mlsanity.com/paper/2410.22971/) (2024)
- [Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text](https://mlsanity.com/paper/2405.19831/) (2024)
- [NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human](https://mlsanity.com/paper/2406.03749/) (2024)
- [On the Impact of Noise in Differentially Private Text Rewriting](https://mlsanity.com/paper/2501.19022/) (2025)
- [Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting](https://mlsanity.com/paper/2503.22379/) (2025)
- [A Survey on Current Trends and Recent Advances in Text Anonymization](https://mlsanity.com/paper/2508.21587/) (2025)
- [Trade-Offs Between Fairness and Privacy in Language Modeling](https://mlsanity.com/paper/2305.14936/) (2023)
- [Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting](https://mlsanity.com/paper/2303.03053/) (2023)
- [Private Language Models via Truncated Laplacian Mechanism](https://mlsanity.com/paper/2410.08027/) (2024)
- [Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees](https://mlsanity.com/paper/2508.20736/) (2025)
- [A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation](https://mlsanity.com/paper/2605.01065/) (2026)
- [IDT: Dual-Task Adversarial Attacks for Privacy Protection](https://mlsanity.com/paper/2406.19642/) (2024)
- [Characterizing Stereotypical Bias from Privacy-preserving Pre-Training](https://mlsanity.com/paper/2407.00764/) (2024)
- [With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting](https://mlsanity.com/paper/2511.00487/) (2025)
- [The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques](https://mlsanity.com/paper/2603.08207/) (2026)
- [Differentially Private Datastore Generation for Retrieval-Augmented Inference](https://mlsanity.com/paper/2606.01413/) (2026)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)
- [The Privacy-Hallucination Tradeoff in Differentially Private Language Models](https://mlsanity.com/paper/2609.00492/) (2026)
- [DP-IPI: A Hybrid Differential Privacy Text Rewriting Mechanism for Indirect Personal Identifiers in Clinical Texts](https://mlsanity.com/paper/2609.29684/) (2026)

## Abstract (arXiv)

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.