Secure Federated Graph-Filtering for Recommender Systems

• URL: http://arxiv.org/abs/2501.16888v1
• Date: Tue, 28 Jan 2025 12:18:09 GMT
• Title: Secure Federated Graph-Filtering for Recommender Systems
• Authors: Julien Nicolas, César Sabater, Mohamed Maouche, Sonia Ben Mokhtar, Mark Coates,
• Abstract summary: This work proposes two decentralized frameworks for securely computing critical graph components without centralizing sensitive information. The first approach leverages lightweight Multi-Party Computation and distributed singular vector computations to privately compute key graph filters. The second extends this framework by incorporating low-rank approximations, enabling a trade-off between communication efficiency and predictive performance.
• Score: 15.955127242261808
• License:
• Abstract: Recommender systems often rely on graph-based filters, such as normalized item-item adjacency matrices and low-pass filters. While effective, the centralized computation of these components raises concerns about privacy, security, and the ethical use of user data. This work proposes two decentralized frameworks for securely computing these critical graph components without centralizing sensitive information. The first approach leverages lightweight Multi-Party Computation and distributed singular vector computations to privately compute key graph filters. The second extends this framework by incorporating low-rank approximations, enabling a trade-off between communication efficiency and predictive performance. Empirical evaluations on benchmark datasets demonstrate that the proposed methods achieve comparable accuracy to centralized state-of-the-art systems while ensuring data confidentiality and maintaining low communication costs. Our results highlight the potential for privacy-preserving decentralized architectures to bridge the gap between utility and user data protection in modern recommender systems.

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