2024
DOI: 10.1186/s12911-024-02546-8
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End-to-end pseudonymization of fine-tuned clinical BERT models

Thomas Vakili,
Aron Henriksson,
Hercules Dalianis

Abstract: Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the models to memorize parts of their training data, making them vulnerable to various privacy attacks. This is cause for concern, especially when these models are applied in the clinical domain, where data are very sensitive. Training data pseudonymization is a privacy-pre… Show more

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