Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization
It addresses privacy-utility trade-offs in NLP for data protection, but is incremental as it builds on existing anonymization methods.
This work evaluated pseudonymization techniques, including rule-based and LLM-based methods, on NLP tasks like text classification and summarization, revealing gaps between original and anonymized data in model quality.
This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. 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. We make our code, pseudonymized datasets, and downstream models publicly available