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Massive digital data processing provides a wide range of opportunities andbenefits, but at the cost of endangering personal data privacy. Anonymisationconsists in removing or replacing sensitive information from data, enabling itsexploitation for different purposes while preserving the privacy ofindividuals. Over the years, a lot of automatic anonymisation systems have beenproposed; however, depending on the type of data, the target language or theavailability of training documents, the task remains challenging still. Theemergence of novel deep-learning models during the last two years has broughtlarge improvements to the state of the art in the field of Natural LanguageProcessing. These advancements have been most noticeably led by BERT, a modelproposed by Google in 2018, and the shared language models pre-trained onmillions of documents. In this paper, we use a BERT-based sequence labellingmodel to conduct a series of anonymisation experiments on several clinicaldatasets in Spanish. We also compare BERT to other algorithms. The experimentsshow that a simple BERT-based model with general-domain pre-training obtainshighly competitive results without any domain specific feature engineering.

Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT