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

TL;DR

This paper benchmarks transformer-based models and traditional approaches for text anonymisation, comparing their performance on the CoNLL-2003 dataset to guide future research and application choices.

Contribution

It provides a comprehensive comparison of modern transformer-based models and traditional architectures for text anonymisation, highlighting their respective strengths and weaknesses.

Findings

Modern models excel at capturing contextual nuances.

Traditional architectures maintain high performance in certain scenarios.

The study offers guidance for selecting appropriate anonymisation models.

Abstract

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.