Paper Info
Title
AHEad: Privacy-preserving online behavioural advertising using homomorphic encryption
Abstract
Online advertising is a rapidly growing industry, forming the primary source of income for many publishers that offer free web content. The practice of serving advertisements based on individuals' interests greatly improves the expected effectiveness of advertisements, and is believed to be beneficial to publishers and users alike. However, the widespread data collection required for such behavioural advertising sparks concerns over user privacy. In this paper, we present AHEad, a privacy-preserving protocol for Online Behavioural Advertising that ensures user privacy by processing data in encrypted form. AHEad combines homomorphic encryption with a machine learning method commonly encountered in existing advertising systems. Advertisements are served based on detailed user profiles, while achieving performance linear in the size of user profiles. To the best of our knowledge, AHEad is the first protocol that preserves user privacy in behavioural advertising while allowing the use of detailed user profiles and machine learning methods.
Year
DOI
Venue
2017
10.1109/WIFS.2017.8267662
2017 IEEE Workshop on Information Forensics and Security (WIFS)
Keywords
Field
DocType
AHEad,privacy-preserving online behavioural advertising,homomorphic encryption,Online advertising,publishers,free web content,advertisements,widespread data collection,privacy-preserving protocol,encrypted form,machine learning method,advertising systems,detailed user profiles,user privacy
Homomorphic encryption,Data collection,Advertising,Computer science,Online advertising,Encryption,Information privacy,Web content,User privacy
Conference
ISSN
ISBN
Citations 
2157-4766
978-1-5090-6770-1
0
PageRank 
References 
Authors
0.34
13
3
Name
Order
Citations
PageRank
Leon J. Helsloot111.07
Gamze Tillem202.03
Zekeriya Erkin357939.17