Designing for User Privacy: Integrating Differential Privacy into Ad Measurement Systems in Practice

Advertisers deploy ad campaigns across many advertising platforms to increase their reach. A multi-touch advertising measurement system is extensively used in practice to assess which ad exposure, amid several platforms, contributes to the final desired user actions such as purchases, sign-ups, and downloads. This attribution process involves tracing users' actions such as views, clicks, and purchases across platforms using tools like pixels and cookies. However, cross-site user tracking has raised increasing privacy concerns regarding the potential misuse of personal information. These concerns have led to legislative actions such as GDPR and industry initiatives like Apple's App Tracking Transparency.

We propose an initiative that provides formal privacy guarantees for cross-site advertising measurement outcomes, with a specific focus on real-time reporting in practical advertising campaigns. This proposal maintains the utility of the practical systems while offering formal and stronger user-level privacy guarantees through differential privacy. Experiments conducted with publicly available real-world advertising campaign datasets demonstrate the effectiveness of this proposal in providing formal privacy guarantees and increasing measurement accuracy, thereby advancing the state-of-the-art in privacy-preserving advertising measurement.

Authors: Jian Du and Shikun Zhang