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.

翻译：构建基于图神经网络（GNN）且不侵犯用户隐私的推荐系统具有挑战性。现有方法可分为联邦图神经网络与分布式图神经网络两类，但两者均存在通信效率低下及隐私泄漏等不良影响。本文提出DGREC——一种面向隐私保护推荐的新型分布式图神经网络，用户可选择公开其交互记录。该方法包含三个阶段：图构建、局部梯度计算及全局梯度传递。第一阶段为每个用户构建本地内部项目超图与全局用户间图；第二阶段在本地设备上建模用户偏好并计算梯度；第三阶段设计名为安全梯度共享的本地差分隐私机制，该机制可证明对用户隐私数据具有强隐私保护性。我们在三个公开数据集上进行大量实验，验证了本框架的持续优越性。