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BAdASS: preserving privacy in behavioural advertising with applied secret sharing. (English) Zbl 1443.94093
Baek, Joonsang (ed.) et al., Provable security. 12th international conference, ProvSec 2018, Jeju, South Korea, October 25–28, 2018. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11192, 397-405 (2018).
Summary: 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.
For the entire collection see [Zbl 1398.94007].
MSC:
94A62 Authentication, digital signatures and secret sharing
94A60 Cryptography
68P27 Privacy of data
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