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

2024-06-04

arXiv.org

AI Summary

This work addresses the tension between privacy preservation and measurement accuracy in real-time cross-platform advertising attribution. We formulate streaming ad-effect measurement as a user-level differential privacy (DP) problem—the first such formulation—and propose AdsBPC, a novel mechanism that designs globally optimal noise allocation under non-i.i.d. user behavior assumptions, strictly satisfying user-level DP while substantially improving estimation accuracy. Our approach integrates differential privacy theory, streaming computation, and adaptive noise optimization to achieve low-latency, high-fidelity attribution. Experiments on both real-world advertising logs and synthetic datasets demonstrate that AdsBPC improves accuracy by 33%–95% over state-of-the-art streaming DP methods, while providing strong formal privacy guarantees and practical feasibility for industrial deployment.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Online Advertising
• Privacy Protection
• Effect Measurement

Innovation

• AdsBPC
• Privacy Protection
• Accuracy Improvement