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This position paper investigates the problem of automated text anonymisation, which is a pre-requisite for secure sharing of documents con-taining sensitive information about individuals. We summarise the key concepts behind text anonymisation and provide a review of current approaches.
Text anonymisation refers to redacting - and potentially replacing - personally identifiable information (PII) within text data. Since such information is the crucial concern of data protection regulations – data is protected if and only if it can be associated with a living individual – its removal means that such data can be freely shared.
27 sie 2022 · The privacy protection toolkit, AnonyMate, is presented, which is built to anonymize both personal identifying information (PII) as well as corporate identifying information (CII) in human-computer dialogue text data.
27 sie 2022 · an open-source text anonymisation tool that anonymises text data in a fully automated way. Textw ash anonymises texts in a semantically-meaningful manner, ensuring that the anonymised...
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.
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
9 maj 2024 · 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.
