  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 Ads-BPC, a novel
user-level differential privacy protection scheme for 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, Ads-BPC achieves a 25%
to 50% 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.
