P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networks

• URL: http://arxiv.org/abs/2410.13905v1
• Date: Wed, 16 Oct 2024 12:29:22 GMT
• Title: P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networks
• Authors: Zheng Wang, Wanwan Wang, Yimin Huang, Zhaopeng Peng, Ziqi Yang, Cheng Wang, Xiaoliang Fan,
• Abstract summary: We propose a novel vertical social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) Experiments on four real-world datasets demonstrate that P4GCN outperforms state-of-the-art methods in terms of recommendation accuracy.
• Score: 17.901647467076476
• License:
• Abstract: 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. The code is available at https://github.com/WwZzz/P4GCN.

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