保护隐私的跨平台图神经网络推荐，无需共享敏感社交数据
P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network
- 采用双方加密图卷积框架，实现隐私保护下的协同建模
- 在四个真实数据集上超越现有方法，推荐准确率显著提升
- 适合关注隐私安全的社交推荐系统开发者与研究者
近年来，图神经网络（GNN）被广泛用于社交推荐系统。然而，现实场景中用户隐私与商业限制常导致无法直接获取其他平台的社交信息。尽管已有许多方法解决了基于矩阵分解的社交推荐问题，但在不直接访问社交数据的前提下构建GNN联邦推荐模型仍鲜有研究。为此，我们提出一种新型垂直联邦社交推荐方法P4GCN，利用隐私保护的两方图卷积网络，在无需直接访问敏感社交信息的情况下提升推荐准确率。首先，引入Sandwich-Encryption模块，确保协同计算过程中的数据全面隐私。其次，对隐私保障进行理论分析，涵盖好奇和诚实参与方的情况。在四个真实数据集上的大量实验表明，P4GCN在推荐准确率上优于现有最先进方法。
原文摘要 · Abstract (English)
In recent years, graph neural networks (GNNs) have been commonly utilized for social recommendation systems. However, real-world scenarios often present challenges related to user privacy and business constraints, inhibiting direct access to valuable social information from other platforms. While many existing methods have tackled matrix factorization-based social recommendations without direct social data access, developing GNN-based federated social recommendation models under similar conditions remains largely unexplored. To address this issue, we propose a novel vertical federated social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) to enhance recommendation accuracy without requiring direct access to sensitive social information. First, we introduce a Sandwich-Encryption module to ensure comprehensive data privacy during the collaborative computing process. Second, we provide a thorough theoretical analysis of the privacy guarantees, considering the participation of both curious and honest parties. Extensive experiments on four real-world datasets demonstrate that P4GCN outperforms state-of-the-art methods in terms of recommendation accuracy.
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