BAdASS

Abstract

Online advertising forms the primary source of income for many publishers offering free web content by serving advertisements tailored to users’ interests. The privacy of users, however, is threatened by the widespread collection of data that is required for behavioural advertising. In this paper, we present BAdASS, a novel privacy-preserving protocol for Online Behavioural Advertising that achieves significant performance improvements over the state-of-the-art without disclosing any information about user interests to any party. BAdASS ensures user privacy by combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.