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Reports (Research Report) Year : 2018

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-01767059 , version 1 (15-04-2018)
hal-01767059 , version 2 (25-04-2018)
hal-01767059 , version 3 (07-05-2018)

Identifiers

  • HAL Id : hal-01767059 , version 3

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!. [Research Report] Inria Rennes - Bretagne Atlantique. 2018, pp.1-12. ⟨hal-01767059v3⟩
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