Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought
<|reference_start|>Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought: Recommender systems have been successfully applied in many applications. Nonetheless, recent studies demonstrate that recommender systems are vulnerable to membership inference attacks (MIAs), leading to the leakage of users' membership privacy. However, existing MIAs relying on shadow training suffer a large performance drop when the attacker lacks knowledge of the training data distribution and the model architecture of the target recommender system. To better understand the privacy risks of recommender systems, we propose shadow-free MIAs that directly leverage a user's recommendations for membership inference. Without shadow training, the proposed attack can conduct MIAs efficiently and effectively under a practice scenario where the attacker is given only black-box access to the target recommender system. The proposed attack leverages an intuition that the recommender system personalizes a user's recommendations if his historical interactions are used by it. Thus, an attacker can infer membership privacy by determining whether the recommendations are more similar to the interactions or the general popular items. We conduct extensive experiments on benchmark datasets across various recommender systems. Remarkably, our attack achieves far better attack accuracy with low false positive rates than baselines while with a much lower computational cost.<|reference_end|>
@article{chi2024shadow-free,
    title={Shadow-Free Membership Inference Attacks: Recommender Systems Are More
  Vulnerable Than You Thought},
    author={Xiaoxiao Chi and Xuyun Zhang and Yan Wang and Lianyong Qi and Amin
  Beheshti and Xiaolong Xu and Kim-Kwang Raymond Choo and Shuo Wang and
  Hongsheng Hu},
    journal={arXiv preprint arXiv:2405.07018},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07018},
    primaryClass={cs.CR}
}