# Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study

arXiv: https://arxiv.org/abs/2405.06709 · HTML: https://mlsanity.com/paper/2405.06709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 3 of 3)

- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [Deep Learning Transformer Architecture for Named Entity Recognition on Low Resourced Languages: State of the art results](https://mlsanity.com/paper/2111.00830/) (2021)

## Built on by (most cited first, 1 of 1)

- [Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection](https://mlsanity.com/paper/2604.09016/) (2026)

## Abstract (arXiv)

In the digital era, with escalating privacy concerns, it's imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual information. This research embarks on a comprehensive examination of text anonymisation methods, focusing on Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and the transformative capabilities of the Transformers architecture. Each model presents unique strengths since LSTM is modeling long-term dependencies, CRF captures dependencies among word sequences, ELMo delivers contextual word representations using deep bidirectional language models and Transformers introduce self-attention mechanisms that provide enhanced scalability. Our study is positioned as a comparative analysis of these models, emphasising their synergistic potential in addressing text anonymisation challenges. Preliminary results indicate that CRF, LSTM, and ELMo individually outperform traditional methods. The inclusion of Transformers, when compared alongside with the other models, offers a broader perspective on achieving optimal text anonymisation in contemporary settings.