🤖 AI Summary
This study addresses the challenge of balancing data availability for cybercrime analysis with privacy protection under regulations such as the GDPR by proposing an end-to-end multimodal data processing pipeline. The pipeline integrates speech enhancement, high-precision named entity recognition (NER), and structure-preserving anonymization techniques to construct a compliant dataset from Telegram-collected text, audio, and images. Experimental results demonstrate that the Parakeet model achieves optimal speech transcription performance, while the proposed Transformer-based NER approach attains the highest F1 score in identifying sensitive information. Furthermore, the anonymized data retains essential semantic structures while satisfying regulatory compliance requirements, thereby effectively supporting research on social engineering attack detection.
📝 Abstract
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.