#data-anonymization

#data-anonymization

The article highlights the importance of effective text anonymization and privacy risk assessment methods in natural language processing.

Incorporating text context improves predictions for high privacy risk spans in NLP data.

The study establishes effective indicators for assessing privacy risks in natural language processing.

Using a fine-tuned language model can improve privacy risk assessment in data anonymization through effective sequence labeling.

Overmasking in text sanitization may lead to unnecessary information loss, while many detected spans can remain in clear text with low re-identification risk.