AHEad: Privacy-preserving Online Behavioural Advertising using Homomorphic Encryption

Leon J. Helsloot, Gamze Tillem, Zekeriya Erkin

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2017 IEEE Workshop on Information Forensics and Security, WIFS 2017
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)978-1-5090-6769-5
Publication statusPublished - 2018
EventWIFS 2017: 9th IEEE International Workshop on Information Forensics and Security - Rennes, France
Duration: 4 Dec 20177 Dec 2017
Conference number: 9


WorkshopWIFS 2017
Internet address


  • Advertising
  • Protocols
  • Encryption
  • Privacy
  • Companies
  • Data privacy


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