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    Graph Embedding for Recommendation against Attribute Inference Attacks

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    Preprintfile1.pdf
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    Type
    Preprint
    Authors
    Zhang, Shijie
    Yin, Hongzhi
    Chen, Tong
    Huang, Zi
    Cui, Lizhen
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2021-01-29
    Permanent link to this record
    http://hdl.handle.net/10754/667194
    
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    Abstract
    In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be naturally modelled as graph-structured data, variants of graph convolutional networks (GCNs) have become a well-established building block in the latest recommenders. Due to the wide utilization of sensitive user profile data, existing recommendation paradigms are likely to expose users to the threat of privacy breach, and GCN-based recommenders are no exception. Apart from the leakage of raw user data, the fragility of current recommenders under inference attacks offers malicious attackers a backdoor to estimate users' private attributes via their behavioral footprints and the recommendation results. However, little attention has been paid to developing recommender systems that can defend such attribute inference attacks, and existing works achieve attack resistance by either sacrificing considerable recommendation accuracy or only covering specific attack models or protected information. In our paper, we propose GERAI, a novel differentially private graph convolutional network to address such limitations. Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks. Furthermore, based on local differential privacy and functional mechanism, we innovatively devise a dual-stage encryption paradigm to simultaneously enforce privacy guarantee on users' sensitive features and the model optimization process. Extensive experiments show the superiority of GERAI in terms of its resistance to attribute inference attacks and recommendation effectiveness.
    Sponsors
    The work has been supported by Australian Research Council (Grant No.DP190101985 and DP170103954).
    Publisher
    arXiv
    arXiv
    2101.12549
    Additional Links
    https://arxiv.org/pdf/2101.12549
    Collections
    Preprints; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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