Sensitivity-Aware Auditing Service for Differentially Private Databases

Differentially private databases (DP-DBs, systems that add mathematical noise to data to protect individual privacy while allowing useful analysis) need auditing services to verify they actually protect privacy as promised, but current approaches don't handle database-specific challenges like varying query sensitivities well. This paper introduces DPAudit, a framework that audits DP-DBs by generating realistic test scenarios, estimating privacy loss parameters, and detecting improper noise injection through statistical testing, even when the database's inner workings are hidden.