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Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!

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Abstract

Recommendation systems help users identify interesting content, but they also open new privacy threats. In this paper, we deeply analyze the effect of a Sybil attack that tries to infer information on users from a user-based collaborative-filtering recommendation systems. We discuss the impact of different similarity metrics used to identity users with similar tastes in the trade-off between recommendation quality and privacy. Finally, we propose and evaluate a novel similarity metric that combines the best of both worlds: a high recommendation quality with a low prediction accuracy for the attacker. Our results, on a state-of-the-art recommendation framework and on real datasets show that existing similarity metrics exhibit a wide range of behaviors in the presence of Sybil attacks, while our new similarity metric consistently achieves the best trade-off while outperforming state-of-the-art solutions.
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Dates and versions

hal-01787060 , version 1 (07-05-2018)

Identifiers

Cite

Antoine Boutet, Florestan de Moor, Davide Frey, Rachid Guerraoui, Anne-Marie Kermarrec, et al.. Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!. DSN 2018 - the 48th International Conference on Dependable Systems and Networks, Jun 2018, Luxembourg, Luxembourg. pp.466-477, ⟨10.1109/DSN.2018.00055⟩. ⟨hal-01787060⟩
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