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

International audienceRecommenders 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

Attacking Recommender Systems with Augmented User Profiles

Recommendation Systems (RS) have become an essential part of many online services. Due to its pivotal role in guiding customers towards purchasing, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this paper, we study the shilling attack: a subsistent and profitable attack where an adversarial party injects a number of user profiles to promote or demote a target item. Conventional shilling attack models are based on simple heuristics that can be easily detected, or directly adopt adversarial attack methods without a special design for RS. Moreover, the study on the attack impact on deep learning based RS is missing in the literature, making the effects of shilling attack against real RS doubtful. We present a novel Augmented Shilling Attack framework (AUSH) and implement it with the idea of Generative Adversarial Network. AUSH is capable of tailoring attacks against RS according to budget and complex attack goals, such as targeting a specific user group. We experimentally show that the attack impact of AUSH is noticeable on a wide range of RS including both classic and modern deep learning based RS, while it is virtually undetectable by the state-of-the-art attack detection model.Comment: CIKM 2020. 10 pages, 2 figure