Making Sense of Private Advertising: A Principled Approach to a Complex Ecosystem

2025-12-23

AI Summary

This work exposes a fundamental trade-off between “perfect privacy” and basic utility in private advertising: existing approaches design targeting and engagement privacy mechanisms in isolation, leading to privacy failures under composition; meanwhile, advertisers’ market research requirements inherently induce information leakage. We formally prove the theoretical lower bound of this trade-off for the first time. To address it, we propose a novel end-to-end privacy alignment paradigm centered on context-sensitive user data and grounded in users’ actual privacy expectations. Our framework integrates formal modeling, compositional privacy analysis, and rigorous privacy–utility trade-off characterization, thereby establishing foundational boundaries for advertising privacy design. It yields verifiable privacy alignment principles and actionable design guidelines for compliant, usable private advertising systems.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Adversarial Learning
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

In this work, we model the end-to-end pipeline of the advertising ecosystem, allowing us to identify two main issues with the current trajectory of private advertising proposals. First, prior work has largely considered ad targeting and engagement metrics individually rather than in composition. This has resulted in privacy notions that, while reasonable for each protocol in isolation, fail to compose to a natural notion of privacy for the ecosystem as a whole, permitting advertisers to extract new information about the audience of their advertisements. The second issue serves to explain the first: we prove that extit{perfect} privacy is impossible for any, even minimally, useful advertising ecosystem, due to the advertisers' expectation of conducting market research on the results. Having demonstrated that leakage is inherent in advertising, we re-examine what privacy could realistically mean in advertising, building on the well-established notion of extit{sensitive} data in a specific context. We identify that fundamentally new approaches are needed when designing privacy-preserving advertising subsystems in order to ensure that the privacy properties of the end-to-end advertising system are well aligned with people's privacy desires.

Problem

• Modeling the advertising ecosystem to identify privacy issues
• Proving perfect privacy is impossible in useful advertising
• Redefining privacy for realistic advertising system design

Innovation

• Modeling the end-to-end advertising pipeline to identify privacy issues
• Proving perfect privacy is impossible in useful advertising ecosystems
• Proposing new approaches for privacy-preserving advertising subsystems