# Textwash -- automated open-source text anonymisation

arXiv: https://arxiv.org/abs/2208.13081 · HTML: https://mlsanity.com/paper/2208.13081/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 27 Aug 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [With Little Power Comes Great Responsibility](https://mlsanity.com/paper/2010.06595/) (2020)
- [Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples](https://mlsanity.com/paper/2004.05887/) (2020)
- [Which *BERT? A Survey Organizing Contextualized Encoders](https://mlsanity.com/paper/2010.00854/) (2020)
- [Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose](https://mlsanity.com/paper/1306.5204/) (2013)
- [Measuring Emotions in the COVID-19 Real World Worry Dataset](https://mlsanity.com/paper/2004.04225/) (2020)
- [No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization](https://mlsanity.com/paper/2103.09263/) (2021)

## Built on by (most cited first, 11 of 11)

- [Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage](https://mlsanity.com/paper/2305.12707/) (2023)
- [Evaluating LLM-based Personal Information Extraction and Countermeasures](https://mlsanity.com/paper/2408.07291/) (2024)
- [Robust Utility-Preserving Text Anonymization Based on Large Language Models](https://mlsanity.com/paper/2407.11770/) (2024)
- [NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human](https://mlsanity.com/paper/2406.03749/) (2024)
- [A Survey on Current Trends and Recent Advances in Text Anonymization](https://mlsanity.com/paper/2508.21587/) (2025)
- [How do we measure privacy in text? A survey of text anonymization metrics](https://mlsanity.com/paper/2512.01109/) (2025)
- [Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification](https://mlsanity.com/paper/2405.01097/) (2024)
- [Protecting De-identified Documents from Search-based Linkage Attacks](https://mlsanity.com/paper/2510.06383/) (2025)
- [Generative Confidants: How do People Experience Trust in Emotional Support from Generative AI?](https://mlsanity.com/paper/2601.16656/) (2026)
- [The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques](https://mlsanity.com/paper/2603.08207/) (2026)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)

## Abstract (arXiv)

The increased use of text data in social science research has benefited from easy-to-access data (e.g., Twitter). That trend comes at the cost of research requiring sensitive but hard-to-share data (e.g., interview data, police reports, electronic health records). We introduce a solution to that stalemate with the open-source text anonymisation software_Textwash_. This paper presents the empirical evaluation of the tool using the TILD criteria: a technical evaluation (how accurate is the tool?), an information loss evaluation (how much information is lost in the anonymisation process?) and a de-anonymisation test (can humans identify individuals from anonymised text data?). The findings suggest that Textwash performs similar to state-of-the-art entity recognition models and introduces a negligible information loss of 0.84%. For the de-anonymisation test, we tasked humans to identify individuals by name from a dataset of crowdsourced person descriptions of very famous, semi-famous and non-existing individuals. The de-anonymisation rate ranged from 1.01-2.01% for the realistic use cases of the tool. We replicated the findings in a second study and concluded that Textwash succeeds in removing potentially sensitive information that renders detailed person descriptions practically anonymous.