Decentralized Graph Neural Network for Privacy-Preserving Recommendation
Xiaolin Zheng Zhongyu Wang Chaochao Chen Jiashu Qian Yao Yang
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.