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.

翻译：推荐系统通常依赖于基于图的滤波器，例如归一化物品-物品邻接矩阵和低通滤波器。尽管这些组件效果显著，但其集中式计算引发了关于用户数据隐私、安全性和伦理使用的担忧。本研究提出了两种去中心化框架，用于安全计算这些关键图组件而无需集中敏感信息。第一种方法利用轻量级多方计算和分布式奇异向量计算来隐私地计算关键图滤波器。第二种方法通过引入低秩近似扩展了该框架，实现了通信效率与预测性能之间的权衡。在基准数据集上的实证评估表明，所提方法在确保数据机密性和维持低通信成本的同时，达到了与集中式最先进系统相当的准确性。我们的结果凸显了隐私保护去中心化架构在现代推荐系统中弥合效用与用户数据保护之间差距的潜力。