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Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization
Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques to better balance the trade-offs between data protection and utility preservation.
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