# Just Rewrite It Again: A Post-Processing Method for Enhanced Semantic Similarity and Privacy Preservation of Differentially Private Rewritten Text

arXiv: https://arxiv.org/abs/2405.19831 · HTML: https://mlsanity.com/paper/2405.19831/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 24 of 24)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [What Does it Mean for a Language Model to Preserve Privacy?](https://mlsanity.com/paper/2202.05520/) (2022)
- [Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations](https://mlsanity.com/paper/1910.08902/) (2019)
- [Differential Privacy for Text Analytics via Natural Text Sanitization](https://mlsanity.com/paper/2106.01221/) (2021)
- [Locally Differentially Private Document Generation Using Zero Shot Prompting](https://mlsanity.com/paper/2310.16111/) (2023)
- [Selective Differential Privacy for Language Modeling](https://mlsanity.com/paper/2108.12944/) (2021)
- [Generalised Differential Privacy for Text Document Processing](https://mlsanity.com/paper/1811.10256/) (2018)
- [TEM: High Utility Metric Differential Privacy on Text](https://mlsanity.com/paper/2107.07928/) (2021)
- [A Differentially Private Text Perturbation Method Using a Regularized Mahalanobis Metric](https://mlsanity.com/paper/2010.11947/) (2020)
- [ER-AE: Differentially Private Text Generation for Authorship Anonymization](https://mlsanity.com/paper/1907.08736/) (2019)
- [ADePT: Auto-encoder based Differentially Private Text Transformation](https://mlsanity.com/paper/2102.01502/) (2021)
- [DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting](https://mlsanity.com/paper/2208.10400/) (2022)
- [DP-BART for Privatized Text Rewriting under Local Differential Privacy](https://mlsanity.com/paper/2302.07636/) (2023)
- [SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining](https://mlsanity.com/paper/1805.00904/) (2018)
- [On a Utilitarian Approach to Privacy Preserving Text Generation](https://mlsanity.com/paper/2104.11838/) (2021)
- [Sentence-level Privacy for Document Embeddings](https://mlsanity.com/paper/2205.04605/) (2022)
- [1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy](https://mlsanity.com/paper/2405.01678/) (2024)
- [Research Challenges in Designing Differentially Private Text Generation Mechanisms](https://mlsanity.com/paper/2012.05403/) (2020)
- [A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings](https://mlsanity.com/paper/2309.10551/) (2023)

## Built on by (most cited first, 9 of 9)

- [PII-Bench: Evaluating Query-Aware Privacy Protection Systems](https://mlsanity.com/paper/2502.18545/) (2025)
- [On the Impact of Noise in Differentially Private Text Rewriting](https://mlsanity.com/paper/2501.19022/) (2025)
- [Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees](https://mlsanity.com/paper/2508.20736/) (2025)
- [DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting](https://mlsanity.com/paper/2503.04990/) (2025)
- [STAMP: Selective Task-Aware Mechanism for Text Privacy](https://mlsanity.com/paper/2603.12237/) (2026)
- [Investigating User Perspectives on Differentially Private Text Privatization](https://mlsanity.com/paper/2503.09338/) (2025)
- [The Double-edged Sword of LLM-based Data Reconstruction: Understanding and Mitigating Contextual Vulnerability in Word-level Differential Privacy Text Sanitization](https://mlsanity.com/paper/2508.18976/) (2025)
- [RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting](https://mlsanity.com/paper/2508.19286/) (2025)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)

## Abstract (arXiv)

The study of Differential Privacy (DP) in Natural Language Processing often views the task of text privatization as a textitrewriting task, in which sensitive input texts are rewritten to hide explicit or implicit private information. In order to evaluate the privacy-preserving capabilities of a DP text rewriting mechanism, textitempirical privacy tests are frequently employed. In these tests, an adversary is modeled, who aims to infer sensitive information (e.g., gender) about the author behind a (privatized) text. Looking to improve the empirical protections provided by DP rewriting methods, we propose a simple post-processing method based on the goal of aligning rewritten texts with their original counterparts, where DP rewritten texts are rewritten textitagain. Our results show that such an approach not only produces outputs that are more semantically reminiscent of the original inputs, but also texts which score on average better in empirical privacy evaluations. Therefore, our approach raises the bar for DP rewriting methods in their empirical privacy evaluations, providing an extra layer of protection against malicious adversaries.