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

arXiv:2404.14465v1 Announce Type: new
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 …

abstract advanced architectures arxiv benchmarking comparative study cs.ai cs.cl cs.ir data data privacy deep learning novel paper privacy realm study tasks text transformer type