计算机科学
超图
差别隐私
图形
推荐系统
信息隐私
隐私保护
人工神经网络
数据共享
数据挖掘
理论计算机科学
人工智能
情报检索
计算机安全
数学
离散数学
医学
替代医学
病理
作者
Xiaolin Zheng,Z. Wang,Chaochao Chen,Jixin Qian,Yao Yang
标识
DOI:10.1145/3583780.3614834
摘要
Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework.
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