  Text anonymization is crucial for sharing sensitive data while maintaining
privacy. Existing techniques face the emerging challenges of re-identification
attack ability of Large Language Models (LLMs), which have shown advanced
capability in memorizing detailed information and patterns as well as
connecting disparate pieces of information. In defending against LLM-based
re-identification attacks, anonymization could jeopardize the utility of the
resulting anonymized data in downstream tasks -- the trade-off between privacy
and data utility requires deeper understanding within the context of LLMs. This
paper proposes a framework composed of three LLM-based components -- a privacy
evaluator, a utility evaluator, and an optimization component, which work
collaboratively to perform anonymization. To provide a practical model for
large-scale and real-time environments, we distill the anonymization
capabilities into a lightweight model using Direct Preference Optimization
(DPO). Extensive experiments demonstrate that the proposed models outperform
baseline models, showing robustness in reducing the risk of re-identification
while preserving greater data utility in downstream tasks. Our code and dataset
are available at https://github.com/UKPLab/arxiv2024-rupta.
