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

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.

Published in:
2018 48Th Annual Ieee/Ifip International Conference On Dependable Systems And Networks (Dsn), 466-477
Presented at:
48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Luxembourg City, LUXEMBOURG, Jun 25-28, 2018
Jan 01 2018
New York, IEEE

 Record created 2019-09-29, last modified 2020-04-20

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