BAdASS: Preserving Privacy in Behavioural Advertising with Applied Secret Sharing

Leon J. Helsloot, Gamze Tillem⁺, and Zekeriya Erkin

Cyber Security Group, Delft University of Technology, Delft, The Netherlands

leonhelsloot@gmail.com, {g.tillem, z.erkin}@tudelft.nl

Abstract

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.

Keywords: Behavioural advertising, Machine learning, Secret sharing, Privacy, Cryptography

+: Corresponding author: Gamze Tillem
Cyber Security Group, Faculty of Electrical Engineering, Mathematics and Computer Science,
Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands,
Tel: +31-15-27-81537

Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA)

Vol. 10, No. 1, pp. 23-41, March 2019 [pdf]

Received: December 28, 2018; Accepted: March 5, 2019; Published: March 31, 2019

DOI: 10.22667/JOWUA.2019.03.31.023