Resource-Efficient Anonymization of Textual Data via Knowledge Distillation from Large Language Models

Protecting personal and sensitive information in textual data is increasingly crucial, especially when leveraging large language models (LLMs) that may pose privacy risks due to their API-based access. We introduce a novel approach and pipeline for anonymizing text across arbitrary domains without the need for manually labeled data or extensive computational resources. Our method employs knowledge distillation from LLMs into smaller encoder-only models via named entity recognition (NER) coupled with regular expressions to create a lightweight model capable of effective anonymization while preserving the semantic and contextual integrity of the data. This reduces computational overhead, enabling deployment on less powerful servers or even personal computing devices. Our findings suggest that knowledge distillation offers a scalable, resource-efficient pathway for anonymization, balancing privacy preservation with model performance and computational efficiency.