Recently, recommender systems have achieved promising performances and become
one of the most widely used web applications. However, recommender systems are
often trained on highly sensitive user data, thus potential data leakage from
recommender systems may lead to severe privacy problems.

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In this paper, we make the first attempt on quantifying the privacy leakage
of recommender systems through the lens of membership inference. In contrast
with traditional membership inference against machine learning classifiers, our
attack faces two main differences. First, our attack is on the user-level but
not on the data sample-level. Second, the adversary can only observe the
ordered recommended items from a recommender system instead of prediction
results in the form of posterior probabilities. To address the above
challenges, we propose a novel method by representing users from relevant
items. Moreover, a shadow recommender is established to derive the labeled
training data for training the attack model. Extensive experimental results
show that our attack framework achieves a strong performance. In addition, we
design a defense mechanism to effectively mitigate the membership inference
threat of recommender systems.

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