The Privacy-Utility Trade-off in the Topics API

Abstract

The ongoing deprecation of third-party cookies by web browser vendors has sparked the proposal of alternative methods to support more privacy-preserving personalized advertising on web browsers and applications. The Topics API is being proposed by Google to provide third-parties with "coarse-grained advertising topics that the page visitor might currently be interested in". In this paper, we analyze the re-identification risks for individual Internet users and the utility provided to advertising companies by the Topics API, i.e. learning the most popular topics and distinguishing between real and random topics. We provide theoretical results dependent only on the API parameters that can be readily applied to evaluate the privacy and utility implications of future API updates, including novel general upper-bounds that account for adversaries with access to unknown, arbitrary side information, the value of the differential privacy parameter $\epsilon$, and experimental results on real-world data that validate our theoretical model.

CCS '24 (to appear)

References in corpus (4)

• Differential Privacy: An Economic Method for Choosing Epsilon
• Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata
• A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow
• A Quantitative Information Flow Analysis of the Topics API

Cited by in corpus (1)

• Differentially Private Synthetic Data Release for Topics API Outputs