The growing use of large language models has increased interest in sharing textual data in a privacy-preserving manner. One prominent line of work addresses this challenge through text rewriting under Local Differential Privacy (LDP), where input texts are locally obfuscated before release with formal privacy guarantees. These guarantees are typically expressed by a parameter $\varepsilon$ that upper bounds the worst-case privacy loss. However, nominal $\varepsilon$ values are often difficult to interpret and compare across mechanisms. In this work, we investigate how to empirically calibrate across text rewriting mechanisms under LDP. We propose TeDA, which formulates calibration via a hypothesis-testing framework that instantiates text distinguishability audits in both surface and embedding spaces, enabling empirical assessment of indistinguishability from privatized texts. Applying this calibration to several representative mechanisms, we demonstrate that similar nominal $\varepsilon$ bounds can imply very different levels of distinguishability. Empirical calibration thus provides a more comparable footing for evaluating privacy-utility trade-offs, as well as a practical tool for mechanism comparison and analysis in real-world LDP text rewriting deployments.

翻译：大型语言模型的广泛应用增加了对隐私保护方式下共享文本数据的兴趣。一条主要研究路线通过局部差分隐私下的文本重写来解决这一挑战，其中输入文本在发布前进行本地模糊处理，并附带形式化的隐私保证。这些保证通常由参数 ε 表示，该参数限定了最坏情况下的隐私损失上界。然而，名义上的 ε 值往往难以解释，且难以在不同机制间进行比较。在本工作中，我们研究了如何在局部差分隐私下对文本重写机制进行经验校准。我们提出了 TeDA，该方法通过假设检验框架形式化校准过程，在表面空间和嵌入空间中实例化文本可区分性审计，从而能够从隐私化文本中经验性地评估不可区分性。将该校准应用于几种代表性机制后，我们证明相似的名义 ε 界限可能对应截然不同的可区分性水平。因此，经验校准为评估隐私-效用权衡提供了更具可比性的基础，也为现实世界局部差分隐私文本重写部署中的机制比较与分析提供了实用工具。