论文标题
PRIVMVMF：推荐系统的隐私保护多视图矩阵分解
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems
论文作者
论文摘要
随着对数据隐私的越来越重视，已经在联合学习（FL）框架中对推荐系统进行了试点研究，在该框架中，多方在不共享数据的情况下协作训练模型。这些研究中的大多数都认为传统的FL框架可以完全保护用户隐私。但是，基于我们的研究，在联合推荐系统的基质分解中存在严重的隐私风险。本文首先在联合推荐系统中的四个方案中对服务器重建攻击进行了严格的理论分析，然后进行了全面的实验。经验结果表明，FL服务器可以根据FL节点上载的梯度准确地> 80％推断用户的信息。鲁棒性分析表明，我们的重建攻击分析在Laplace噪声下的随机猜测优于30％的猜测，而B对于所有情况，B大于0.5。然后，本文提出了一个基于同构加密，隐私保护多视图矩阵分解（PRIVMVMF）的新隐私保护框架，以增强联合推荐系统中的用户数据隐私保护。拟议的PREPMVMF通过Movielens数据集成功实施和测试。
With an increasing focus on data privacy, there have been pilot studies on recommender systems in a federated learning (FL) framework, where multiple parties collaboratively train a model without sharing their data. Most of these studies assume that the conventional FL framework can fully protect user privacy. However, there are serious privacy risks in matrix factorization in federated recommender systems based on our study. This paper first provides a rigorous theoretical analysis of the server reconstruction attack in four scenarios in federated recommender systems, followed by comprehensive experiments. The empirical results demonstrate that the FL server could infer users' information with accuracy >80% based on the uploaded gradients from FL nodes. The robustness analysis suggests that our reconstruction attack analysis outperforms the random guess by >30% under Laplace noises with b no larger than 0.5 for all scenarios. Then, the paper proposes a new privacy-preserving framework based on homomorphic encryption, Privacy-Preserving Multi-View Matrix Factorization (PrivMVMF), to enhance user data privacy protection in federated recommender systems. The proposed PrivMVMF is successfully implemented and tested thoroughly with the MovieLens dataset.
