Clustering for Private Interest-based Advertising
Alessandro Epasto, Andrés Muñoz Medina, Steven G. Avery, Yijian Bai, Róbert Busa‐Fekete, CJ Carey, Ya Gao, David Guthrie, Subham Ghosh, James Ioannidis, Junyi Jiao, Jakub Łącki, Jason Lee, Arne Mauser, Brian Milch, Vahab Mirrokni, Deepak Ravichandran, Wei Shi, Max Spero, Yunting Sun · 2021
We study the problem of designing privacy-enhanced solutions for interest-based advertisement (IBA). IBA is a key component of the online ads ecosystem and provides a better ad experience to users. Indeed, IBA enables advertisers to show users impressions that are relevant to them. Nevertheless, the current way ad tech companies achieve this is by building detailed interest profiles for individual users. In this work we ask whether such fine grained personalization is required, and present mechanisms that achieve competitive performance while giving privacy guarantees to the end users. More precisely we present the first detailed exploration of how to implement Chrome's Federated Learning of Cohorts (FLoC) API. We define the privacy properties required for the API and evaluate multiple hashing and clustering algorithms discussing the trade-offs between utility, privacy, and ease of implementation.