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

TL;DR

This paper introduces P4GCN, a privacy-preserving federated GNN model for social recommendation that enhances accuracy without exposing sensitive social data, using novel encryption and theoretical privacy guarantees.

Contribution

We propose a novel vertical federated GNN model with a unique encryption scheme and privacy analysis, addressing privacy concerns in federated social recommendation.

Findings

P4GCN outperforms existing methods in recommendation accuracy.

The Sandwich-Encryption module ensures data privacy during collaboration.

Theoretical analysis confirms privacy guarantees for both parties.

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.