Privacy in Web Advertising: Analytics and Modeling

Abstract

Privacy in general, and differential privacy (DP) in particular, have become important topics in data mining and machine learning. Digital advertising is a critical component of the internet and is powered by large-scale data analytics and machine learning models; privacy concerns around these are on the rise. Despite the central importance of private ad analytics and training privacy-preserving ad prediction models, there has been relatively little exposure of this subject to the broader Web community. In the past three years, the interest in privacy and the interest in online advertising have been steadily growing. The aim of this tutorial is to provide researchers with an introduction to the problems that arise in private analytics and modeling in advertising, survey recent results, and describe the main research challenges in the space.