Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization

Oleksandr Yermilov, Vipul Raheja, Artem Chernodub

This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a var…