Paper 2018/744

BAdASS: Preserving Privacy in Behavioural Advertising with Applied Secret Sharing

Leon J. Helsloot, Gamze Tillem, and Zekeriya Erkin


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, which benefits both publishers and users. 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.

Available format(s)
Publication info
Published elsewhere. MAJOR revision.The 12th International Conference on Provable Security (ProvSec 2018)
Behavioural AdvertisingMachine LearningSecret sharingPrivacyCryptography
Contact author(s)
g tillem @ tudelft nl
2018-08-15: received
Short URL
Creative Commons Attribution


      author = {Leon J.  Helsloot and Gamze Tillem and Zekeriya Erkin},
      title = {BAdASS: Preserving Privacy in Behavioural Advertising with Applied Secret Sharing},
      howpublished = {Cryptology ePrint Archive, Paper 2018/744},
      year = {2018},
      note = {\url{}},
      url = {}
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