# Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches

arXiv: https://arxiv.org/abs/2404.14465 · HTML: https://mlsanity.com/paper/2404.14465/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization](https://mlsanity.com/paper/2001.08053/) (2020)
- [Conversational Feedback in Scripted versus Spontaneous Dialogues: A Comparative Analysis](https://mlsanity.com/paper/2309.15656/) (2023)

## Built on by (most cited first, 5 of 5)

- [PII-Bench: Evaluating Query-Aware Privacy Protection Systems](https://mlsanity.com/paper/2502.18545/) (2025)
- [A Survey on Current Trends and Recent Advances in Text Anonymization](https://mlsanity.com/paper/2508.21587/) (2025)
- [TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi](https://mlsanity.com/paper/2502.04245/) (2025)
- [Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection](https://mlsanity.com/paper/2604.09016/) (2026)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)

## Abstract (arXiv)

In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there is a burgeoning interest in leveraging these advanced models for text anonymisation tasks. This paper presents a comprehensive benchmarking study comparing the performance of transformer-based models and Large Language Models(LLM) against traditional architectures for text anonymisation. Utilising the CoNLL-2003 dataset, known for its robustness and diversity, we evaluate several models. Our results showcase the strengths and weaknesses of each approach, offering a clear perspective on the efficacy of modern versus traditional methods. Notably, while modern models exhibit advanced capabilities in capturing con textual nuances, certain traditional architectures still keep high performance. This work aims to guide researchers in selecting the most suitable model for their anonymisation needs, while also shedding light on potential paths for future advancements in the field.