Online advertising is a multi-billion dollar industry, forming the primary source of income for many publishers offering free web content. Serving advertisements tailored to users’ interests greatly improves the effectiveness of advertisements, and is believed to be beneficial to publishers and users alike. 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 processing data within the secret-shared domain, using the heavily fragmented shape of the online advertising landscape to its advantage and combining efficient secret-sharing techniques with a machine learning method commonly encountered in existing advertising systems. Our protocol serves advertisements within a fraction of a second, based on highly detailed user profiles and widely used machine learning methods.

Original languageEnglish
Pages (from-to)23-41
Number of pages19
JournalJournal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
Volume10
Issue number1
Publication statusPublished - 2019
EventProvSec 2018: 12th International Conference on Provable Security - Jeju Island, Korea, Republic of
Duration: 25 Oct 201828 Oct 2018
Conference number: 12
https://ssl.informatics.uow.edu.au/provsec2018/index.html

    Research areas

  • Behavioural advertising, Cryptography, Machine learning, Privacy, Secret sharing

ID: 56258650