Recent work in privacy research on large language models has shown that they
achieve near human-level performance at inferring personal data from real-world
online texts. With consistently increasing model capabilities, existing text
anonymization methods are currently lacking behind regulatory requirements and
adversarial threats. This raises the question of how individuals can
effectively protect their personal data in sharing online texts. In this work,
we take two steps to answer this question: We first present a new setting for
evaluating anonymizations in the face of adversarial LLMs inferences, allowing
for a natural measurement of anonymization performance while remedying some of
the shortcomings of previous metrics. We then present our LLM-based adversarial
anonymization framework leveraging the strong inferential capabilities of LLMs
to inform our anonymization procedure. In our experimental evaluation, we show
on real-world and synthetic online texts how adversarial anonymization
outperforms current industry-grade anonymizers both in terms of the resulting
utility and privacy.

近期在隐私研究中，大型语言模型在推断真实世界在线文本中的个人数据方面已经达到接近人类水平的性能。在存在不断增长的模型能力的同时，现有的文本匿名化方法目前无法满足监管要求和对抗威胁。这引发了一个问题，即个人如何能够有效地在分享在线文本时保护自己的个人数据。本研究分两步回答这个问题：首先，我们提出了一个新的环境，在对抗性大型语言模型推理的情况下评估匿名化性能，从而在纠正以前的指标缺陷的同时，实现对匿名化性能的自然度量。然后，我们提出了基于大型语言模型的对抗性匿名化框架，利用大型语言模型的强大推理能力来指导我们的匿名化过程。在我们的实验评估中，我们展示了在真实世界和合成在线文本中，对抗性匿名化在结果效用和隐私方面均优于目前的工业级匿名化工具。