This paper introduces the LDP-Auditor framework for empirically estimating the privacy loss of Locally Differentially Private (LDP) mechanisms. Several factors influencing the privacy audit are explored, such as the impact of different encoding and perturbation functions of eight state-of-the-art LDP protocols. Furthermore, the influence of domain size as well as the theoretical privacy loss parameter $\epsilon$ on local privacy estimation are also examined. Overall, our LDP-Auditor framework and findings offer valuable insights into the sources of randomness and information loss in LDP protocols, contributing to a more realistic understanding of the local privacy loss. Furthermore, we demonstrate the effectiveness of LDP-Auditor by successfully identifying a bug in an LDP library.

翻译：本文提出了LDP-Auditor框架，用于实证估算局部差分隐私（LDP）机制的隐私损失。论文探讨了影响隐私审计的多种因素，例如八种最先进LDP协议中不同编码与扰动函数的影响。此外，还研究了域规模以及理论隐私损失参数$\epsilon$对本地隐私估计的影响。总体而言，我们的LDP-Auditor框架及研究结果为理解LDP协议中随机性的来源与信息损失提供了宝贵见解，有助于更现实地把握本地隐私损失。同时，我们通过成功识别一个LDP库中的缺陷，展示了LDP-Auditor的有效性。