# Generalised Differential Privacy for Text Document Processing

arXiv: https://arxiv.org/abs/1811.10256 · HTML: https://mlsanity.com/paper/1811.10256/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 30 of 52)

- [How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy](https://mlsanity.com/paper/2303.00654/) (2023)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [Differentially Private Language Models for Secure Data Sharing](https://mlsanity.com/paper/2210.13918/) (2022)
- [The Limits of Word Level Differential Privacy](https://mlsanity.com/paper/2205.02130/) (2022)
- [Selective Differential Privacy for Language Modeling](https://mlsanity.com/paper/2108.12944/) (2021)
- [THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption](https://mlsanity.com/paper/2206.00216/) (2022)
- [Natural Language Understanding with Privacy-Preserving BERT](https://mlsanity.com/paper/2104.07504/) (2021)
- [Split-and-Denoise: Protect large language model inference with local differential privacy](https://mlsanity.com/paper/2310.09130/) (2023)
- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://mlsanity.com/paper/2111.02705/) (2021)
- [DP-MLM: Differentially Private Text Rewriting Using Masked Language Models](https://mlsanity.com/paper/2407.00637/) (2024)
- [CAPE: Context-Aware Private Embeddings for Private Language Learning](https://mlsanity.com/paper/2108.12318/) (2021)
- [Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning](https://mlsanity.com/paper/2110.01643/) (2021)
- [On a Utilitarian Approach to Privacy Preserving Text Generation](https://mlsanity.com/paper/2104.11838/) (2021)
- [How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing](https://mlsanity.com/paper/2205.10095/) (2022)
- [Guiding Text-to-Text Privatization by Syntax](https://mlsanity.com/paper/2306.01471/) (2023)
- [Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling](https://mlsanity.com/paper/2101.11310/) (2021)
- [You Are What You Write: Preserving Privacy in the Era of Large Language Models](https://mlsanity.com/paper/2204.09391/) (2022)
- [Differential Privacy in Natural Language Processing: The Story So Far](https://mlsanity.com/paper/2208.08140/) (2022)
- [Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey](https://mlsanity.com/paper/2309.16398/) (2023)
- [Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting](https://mlsanity.com/paper/2305.12723/) (2023)
- [1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy](https://mlsanity.com/paper/2405.01678/) (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)
- [Bootstrapping Text Anonymization Models with Distant Supervision](https://mlsanity.com/paper/2205.06895/) (2022)
- [Universal Exact Compression of Differentially Private Mechanisms](https://mlsanity.com/paper/2405.20782/) (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)
- [Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis](https://mlsanity.com/paper/2310.14312/) (2023)
- [Spend Your Budget Wisely: Towards an Intelligent Distribution of the Privacy Budget in Differentially Private Text Rewriting](https://mlsanity.com/paper/2503.22379/) (2025)
- [On the privacy-utility trade-off in differentially private hierarchical text classification](https://mlsanity.com/paper/2103.02895/) (2021)
- [Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees](https://mlsanity.com/paper/2508.20736/) (2025)

## Abstract (arXiv)

We address the problem of how to "obfuscate" texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text. In this paper we combine ideas from "generalised differential privacy" and machine learning techniques for text processing to model privacy for text documents. We define a privacy mechanism that operates at the level of text documents represented as "bags-of-words" - these representations are typical in machine learning and contain sufficient information to carry out many kinds of classification tasks including topic identification and authorship attribution (of the original documents). We show that our mechanism satisfies privacy with respect to a metric for semantic similarity, thereby providing a balance between utility, defined by the semantic content of texts, with the obfuscation of stylistic clues. We demonstrate our implementation on a "fan fiction" dataset, confirming that it is indeed possible to disguise writing style effectively whilst preserving enough information and variation for accurate content classification tasks.