# 1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy

arXiv: https://arxiv.org/abs/2405.01678 · HTML: https://mlsanity.com/paper/2405.01678/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 2 May 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations](https://mlsanity.com/paper/1910.08902/) (2019)
- [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022)
- [Differential Privacy for Text Analytics via Natural Text Sanitization](https://mlsanity.com/paper/2106.01221/) (2021)
- [Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness](https://mlsanity.com/paper/2010.01285/) (2020)
- [DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass](https://mlsanity.com/paper/2309.06746/) (2023)
- [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)
- [Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text](https://mlsanity.com/paper/1910.08917/) (2019)
- [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)
- [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)
- [I Am Not What I Write: Privacy Preserving Text Representation Learning](https://mlsanity.com/paper/1907.03189/) (2019)
- [Research Challenges in Designing Differentially Private Text Generation Mechanisms](https://mlsanity.com/paper/2012.05403/) (2020)
- [Gradual Release of Sensitive Data under Differential Privacy](https://mlsanity.com/paper/1504.00429/) (2015)

## Built on by (most cited first, 10 of 10)

- [DP-MLM: Differentially Private Text Rewriting Using Masked Language Models](https://mlsanity.com/paper/2407.00637/) (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)
- [Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting](https://mlsanity.com/paper/2503.22379/) (2025)
- [How do we measure privacy in text? A survey of text anonymization metrics](https://mlsanity.com/paper/2512.01109/) (2025)
- [A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy](https://mlsanity.com/paper/2407.00638/) (2024)
- [DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting](https://mlsanity.com/paper/2503.04990/) (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)
- [With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting](https://mlsanity.com/paper/2511.00487/) (2025)
- [A Case Study on the Impact of Anonymization Along the RAG Pipeline](https://mlsanity.com/paper/2604.15958/) (2026)
- [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 privacy-preserving Natural Language Processing (NLP) has gained rising attention in recent years. One promising avenue studies the integration of Differential Privacy in NLP, which has brought about innovative methods in a variety of application settings. Of particular note are textitword-level Metric Local Differential Privacy (MLDP) mechanisms, which work to obfuscate potentially sensitive input text by performing word-by-word textitperturbations. Although these methods have shown promising results in empirical tests, there are two major drawbacks: (1) the inevitable loss of utility due to addition of noise, and (2) the computational expensiveness of running these mechanisms on high-dimensional word embeddings. In this work, we aim to address these challenges by proposing texttt1-Diffractor, a new mechanism that boasts high speedups in comparison to previous mechanisms, while still demonstrating strong utility- and privacy-preserving capabilities. We evaluate texttt1-Diffractor for utility on several NLP tasks, for theoretical and task-based privacy, and for efficiency in terms of speed and memory. texttt1-Diffractor shows significant improvements in efficiency, while still maintaining competitive utility and privacy scores across all conducted comparative tests against previous MLDP mechanisms. Our code is made available at: https://github.com/sjmeis/Diffractor.