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Collaborative Filtering Under a Sybil Attack: Analysis of a Privacy Threat

Davide Frey 1 Rachid Guerraoui 2 Anne-Marie Kermarrec 1 Antoine Rault 1, * 
* Corresponding author
1 ASAP - As Scalable As Possible: foundations of large scale dynamic distributed systems
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : Recommenders have become a fundamental tool to navigate the huge amount of information available on the web. However, their ubiquitous presence comes with the risk of exposing sensitive user information. This paper explores this problem in the context of user-based collaborative filtering. We consider an active attacker equipped with externally available knowledge about the interests of users. The attacker creates fake identities based on this external knowledge and exploits the recommendations it receives to identify the items appreciated by a user. Our experiment on a real data trace shows that while the attack is effective, the inherent similarity between real users may be enough to protect at least part of their interests.
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Submitted on : Tuesday, June 2, 2015 - 3:06:02 PM
Last modification on : Tuesday, October 25, 2022 - 4:23:32 PM
Long-term archiving on: : Monday, April 24, 2017 - 9:13:49 PM


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Davide Frey, Rachid Guerraoui, Anne-Marie Kermarrec, Antoine Rault. Collaborative Filtering Under a Sybil Attack: Analysis of a Privacy Threat. Eighth European Workshop on System Security EuroSec 2015, Apr 2015, Bordeaux, France. ⟨10.1145/2751323.2751328⟩. ⟨hal-01158723⟩



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