# Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection

arXiv: https://arxiv.org/abs/2604.09016 · HTML: https://mlsanity.com/paper/2604.09016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 4 of 4)

- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [Granary: Speech Recognition and Translation Dataset in 25 European Languages](https://mlsanity.com/paper/2505.13404/) (2025)
- [Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches](https://mlsanity.com/paper/2404.14465/) (2024)
- [Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study](https://mlsanity.com/paper/2405.06709/) (2024)

## Abstract (arXiv)

This study addresses the challenge of creating datasets for cybercrime analysis while complying with the requirements of regulations such as the General Data Protection Regulation (GDPR) and Organic Law 10/1995 of the Penal Code. To this end, a system is proposed for collecting information from the Telegram platform, including text, audio, and images; the implementation of speech-to-text transcription models incorporating signal enhancement techniques; and the evaluation of different Named Entity Recognition (NER) solutions, including Microsoft Presidio and AI models designed using a transformer-based architecture. Experimental results indicate that Parakeet achieves the best performance in audio transcription, while the proposed NER solutions achieve the highest f1-score values in detecting sensitive information. In addition, anonymization metrics are presented that allow evaluation of the preservation of structural coherence in the data, while simultaneously guaranteeing the protection of personal information and supporting cybersecurity research within the current legal framework.