Large Language Models are Advanced Anonymizers

Understand

Recent privacy research on large language models (LLMs) has shown that they achieve near-human-level performance at inferring personal data from online texts.

• With ever-increasing model capabilities, existing text anonymization methods are currently lacking behind regulatory requirements and adversarial threats.
• In this work, we take two steps to bridge this gap: First, we present a new setting for evaluating anonymization in the face of adversarial LLM inferences, allowing for a natural measurement of anonymization performance while remedying some of the shortcomings of previous metrics.
• Then, within this setting, we develop a novel LLM-based adversarial anonymization framework leveraging the strong inferential capabilities of LLMs to inform our anonymization procedure.