提出安全去中心化图滤波方法，保护用户隐私同时保持推荐效果。
Secure Federated Graph-Filtering for Recommender Systems
- 用多方计算和分布式奇异向量计算实现图滤波的私密化
- 在多个数据集上达到与集中式系统相当的推荐精度
- 兼顾通信效率与隐私保护，适合注重数据安全的推荐场景
推荐系统常依赖图滤波技术，如归一化物品-物品邻接矩阵和低通滤波器。尽管有效，但这些组件的集中式计算引发用户数据隐私、安全与伦理问题。本文提出两种去中心化框架，可在不集中敏感信息的前提下安全计算关键图组件。第一种方法利用轻量级多方计算与分布式奇异向量计算，实现图滤波的私密计算；第二种通过引入低秩近似，在通信效率与预测性能间实现权衡。在基准数据集上的实证评估表明，所提方法在保持低通信开销的同时，达到与集中式最先进系统相当的准确性，且确保数据机密性。结果表明，隐私保护的去中心化架构有望弥合现代推荐系统中性能与用户数据保护之间的差距。
原文摘要 · Abstract (English)
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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