This paper introduces an empirical framework for auditing privacy leakage in interactive database systems (DBS) that implement differential privacy (DP). Without making any assumptions about the formal DP mechanisms or parameters in use, we simulate an auditor with black-box access to query outputs and evaluate privacy leakage using membership inference attacks (MIAs). Our framework provides empirical lower bounds on the privacy loss parameter e based on attack success, providing a signal of privacy risk even when a theoretical analysis is not available or verifiable. We implement this framework in a system modeled after a major social media company's production environment and show how factors like data distribution, target selection, and query specificity affect the observed privacy. Our work offers a valuable and practical tool for red-teaming and auditing privacy in large-scale opaque DBS.

Data engineering often requires accuracy (utility) constraints on results, posing significant challenges in designing differentially private (DP) mechanisms, particularly under stringent privacy parameter$\epsilon$. In this paper, we propose a privacy-boosting framework that is compatible with most noise-adding DP mechanisms. Our framework enhances the likelihood of outputs falling within a preferred subset of the support to meet utility requirements while enlarging the overall variance to reduce privacy leakage. We characterize the privacy loss distribution of our framework and present the privacy profile formulation for$(\epsilon,\ \delta)-\mathbf{DP}$and Rényi DP (RDP) guarantees. We study special cases involving data-dependent and data-independent utility formulations. Through extensive experiments, we demonstrate that our framework achieves lower privacy loss than standard DP mechanisms under utility constraints. Notably, our approach is particularly effective in reducing privacy loss with large query sensitivity relative to the true answer, offering a more practical and flexible approach to designing differentially private mechanisms that meet specific utility constraints.

Online advertising is a cornerstone of the Internet ecosystem, with advertising measurement playing a crucial role in optimizing efficiency. Ad measurement entails attributing desired behaviors, such as purchases, to ad exposures across various platforms, necessitating the collection of user activities across these platforms. As this practice faces increasing restrictions due to rising privacy concerns, safeguarding user privacy in this context is imperative. Our work is the first to formulate the real-world challenge of advertising measurement systems with real-time reporting of streaming data in advertising campaigns. We introduce AdsBPC, a novel user-level differential privacy protection scheme for online advertising measurement results. This approach optimizes global noise power and results in a non-identically distributed noise distribution that preserves differential privacy while enhancing measurement accuracy. Through experiments on both real-world advertising campaigns and synthetic datasets, AdsBPC achieves a 33% to 95% increase in accuracy over existing streaming DP mechanisms applied to advertising measurement. This highlights our method's effectiveness in achieving superior accuracy alongside a formal privacy guarantee, thereby advancing the state-of-the-art in privacy-preserving advertising measurement.