A Quantitative Information Flow Analysis of the Topics API

TL;DR

This paper applies Quantitative Information Flow analysis to evaluate the privacy implications of Google's Topics API, showing it offers better privacy than traditional third-party cookies while maintaining utility for interest-based advertising.

Contribution

It provides a theoretical QIF framework to analyze the privacy-utility trade-off of the Topics API, a novel approach in this context.

Findings

Topics API has lower re-identification risk than third-party cookies

Theoretical model quantifies privacy benefits of the Topics API

Framework enables analysis of privacy-utility trade-offs

Abstract

Third-party cookies have been a privacy concern since cookies were first developed in the mid 1990s, but more strict cookie policies were only introduced by Internet browser vendors in the early 2010s. More recently, due to regulatory changes, browser vendors have started to completely block third-party cookies, with both Firefox and Safari already compliant. The Topics API is being proposed by Google as an additional and less intrusive source of information for interest-based advertising (IBA), following the upcoming deprecation of third-party cookies. Initial results published by Google estimate the probability of a correct re-identification of a random individual would be below 3% while still supporting IBA. In this paper, we analyze the re-identification risk for individual Internet users introduced by the Topics API from the perspective of Quantitative Information Flow (QIF), an information- and decision-theoretic framework. Our model allows a theoretical analysis of both privacy and utility aspects of the API and their trade-off, and we show that the Topics API does have better privacy than third-party cookies. We leave the utility analyses for future work.