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- W4283650725 abstract "Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. In such attacks, an adversary aims to infer whether a user's data is used to train the target recommender. To achieve this, previous work has used a shadow recommender to derive training data for the attack model, and then predicts the membership by calculating difference vectors between users' historical interactions and recommended items. State-of-the-art methods face two challenging problems: (i) training data for the attack model is biased due to the gap between shadow and target recommenders, and (ii) hidden states in recommenders are not observational, resulting in inaccurate estimations of difference vectors." @default.
- W4283650725 created "2022-06-29" @default.
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- W4283650725 date "2022-08-14" @default.
- W4283650725 modified "2023-10-16" @default.
- W4283650725 title "Debiasing Learning for Membership Inference Attacks Against Recommender Systems" @default.
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- W4283650725 doi "https://doi.org/10.1145/3534678.3539392" @default.
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