State-of-the-art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarantees remains challenging. We introduce a Gaussian Differential Privacy (GDP)-based auditing framework that measures privacy via the full false-positive/false-negative tradeoff. Applied to MST and AIM under worst-case settings, our method provides the first tight audits in the strong-privacy regime. For $(ε,δ)=(1,10^{-2})$, we obtain $μ_{emp}\approx0.43$ vs. implied $μ=0.45$, showing a small theory-practice gap. Our code is publicly available: https://github.com/sassoftware/dpmm.

翻译：最先进的差分隐私（DP）合成数据生成器（如MST和AIM）被广泛使用，但对其隐私保证进行严格审计仍具挑战。我们提出了一种基于高斯差分隐私（GDP）的审计框架，通过完整的假阳性/假阴性权衡来度量隐私。在MST和AIM的最坏情形设置下应用，该方法首次在强隐私制度下提供了严格审计。对于$(ε,δ)=(1,10^{-2})$，我们得到$μ_{emp}\approx0.43$，而理论隐含$μ=0.45$，表明理论与实践差距甚微。我们的代码已公开：https://github.com/sassoftware/dpmm。