BAdASS: Preserving privacy in behavioural advertising with applied secret sharing

Leon J. Helsloot, Gamze Tillem, Zekeriya Erkin

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

4 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProvable Security
Subtitle of host publication12th International Conference, ProvSec 2018 - Proceedings
EditorsJoonsang Baek, Willy Susilo, Jongkil Kim
Place of PublicationCham
Number of pages9
ISBN (Electronic)978-3-030-01446-9
ISBN (Print)978-3-030-01445-2
Publication statusPublished - 2018
EventProvSec 2018: 12th International Conference on Provable Security - Jeju Island, Korea, Republic of
Duration: 25 Oct 201828 Oct 2018
Conference number: 12

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceProvSec 2018
Abbreviated titleProvSEC
Country/TerritoryKorea, Republic of
CityJeju Island
Internet address


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


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