_:n6921723ffdd64b259fcfb202929b506axb83
<http://schema.org/description>
"In recent years, recommender systems play a pivotal role in helping users\nidentify the most suitable items that satisfy personal preferences. As\nuser-item interactions can be naturally modelled as graph-structured data,\nvariants of graph convolutional networks (GCNs) have become a well-established\nbuilding block in the latest recommenders. Due to the wide utilization of\nsensitive user profile data, existing recommendation paradigms are likely to\nexpose users to the threat of privacy breach, and GCN-based recommenders are no\nexception. Apart from the leakage of raw user data, the fragility of current\nrecommenders under inference attacks offers malicious attackers a backdoor to\nestimate users' private attributes via their behavioral footprints and the\nrecommendation results. However, little attention has been paid to developing\nrecommender systems that can defend such attribute inference attacks, and\nexisting works achieve attack resistance by either sacrificing considerable\nrecommendation accuracy or only covering specific attack models or protected\ninformation. In our paper, we propose GERAI, a novel differentially private\ngraph convolutional network to address such limitations. Specifically, in\nGERAI, we bind the information perturbation mechanism in differential privacy\nwith the recommendation capability of graph convolutional networks.\nFurthermore, based on local differential privacy and functional mechanism, we\ninnovatively devise a dual-stage encryption paradigm to simultaneously enforce\nprivacy guarantee on users' sensitive features and the model optimization\nprocess. Extensive experiments show the superiority of GERAI in terms of its\nresistance to attribute inference attacks and recommendation effectiveness."
<https://deepai.org/profile/lizhen-cui>