标题： 用随机后处理净化近似差分隐私 显示英文标题

标题： Purifying Approximate Differential Privacy with Randomized Post-processing

摘要： 我们提出一个框架，将$(\varepsilon, \delta)$-近似差分隐私（DP）机制转换为$(\varepsilon, 0)$-纯差分隐私（DP）机制，这一过程我们称为“净化”。这种算法技术利用带有校准噪声的随机后处理来消除$\delta$参数，同时保持效用。通过结合近似DP机制的更紧效用界限和计算效率以及纯DP的更强保证，我们的方法实现了两全其美。我们在各种场景中展示了该框架的适用性，包括差分隐私经验风险最小化（DP-ERM）、数据依赖的DP机制，如提议-测试-发布（PTR），以及查询发布任务。据我们所知，这是第一项提供系统方法将近似DP转换为纯DP的同时保持竞争力的准确性与计算效率的工作。 显示英文摘要

摘要： We propose a framework to convert $(\varepsilon, \delta)$-approximate Differential Privacy (DP) mechanisms into $(\varepsilon, 0)$-pure DP mechanisms, a process we call ``purification''. This algorithmic technique leverages randomized post-processing with calibrated noise to eliminate the $\delta$ parameter while preserving utility. By combining the tighter utility bounds and computational efficiency of approximate DP mechanisms with the stronger guarantees of pure DP, our approach achieves the best of both worlds. We illustrate the applicability of this framework in various settings, including Differentially Private Empirical Risk Minimization (DP-ERM), data-dependent DP mechanisms such as Propose-Test-Release (PTR), and query release tasks. To the best of our knowledge, this is the first work to provide a systematic method for transforming approximate DP into pure DP while maintaining competitive accuracy and computational efficiency.