Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy

TL;DR

This paper introduces AdsBPC, a novel differential privacy scheme for online advertising measurement that improves accuracy significantly while ensuring user privacy in real-time streaming data scenarios.

Contribution

It is the first to address real-time, user-level differential privacy in online advertising measurement, optimizing noise distribution for better accuracy.

Findings

Achieves 33% to 95% accuracy improvement over existing methods.

Provides formal privacy guarantees with enhanced measurement precision.

Validated on real-world and synthetic datasets.

Abstract

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.