Publicidade Baseada em Interesses

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 ε, and experimental results on real-world data that validate our theoretical model.

This record provides the datasets of the paper The Privacy-Utility Trade-off in the Topics API.

This repository provides the experimental results of the paper The Privacy-Utility Trade-off in the Topics API.

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.