Sensitivity-Aware Auditing Service for Differentially Private Databases

Summary

The paper introduces DPAudit, a sensitivity-aware auditing service framework for evaluating and verifying the privacy guarantees of differentially private databases (DP-DBs). It adds adaptive neighboring dataset generation that reflects real-world query sensitivities, optimized privacy loss estimators for estimating epsilon under Laplace and Gaussian mechanisms, and automated noise detection via statistical hypothesis testing that works even in black-box settings. Experiments reportedly show accurate epsilon estimates with low computational overhead.