论文标题
具有离群值的非阴性矩阵分解的隐私性矩阵
Privacy-preserving Non-negative Matrix Factorization with Outliers
论文作者
论文摘要
非阴性矩阵分解是一种流行的无监督的机器学习算法，用于从固有非负值的数据中提取有意义的特征。但是，此类数据集通常可能包含对隐私敏感的用户数据，因此，我们可能需要采取必要的步骤来确保用户在分析数据时的隐私。在这项工作中，我们专注于在隐私保护框架中开发一种非负矩阵分解算法。更具体地说，我们为非负矩阵分解能够在私人数据上运行的非负矩阵分解提出了一种新颖的隐私算法，同时实现了与非私有算法相当的结果。我们设计该框架以使一个人可以控制基于公用事业差距的隐私受赠人的程度。我们在六个真实的数据集中显示了我们提出的框架的性能。实验结果表明，我们提出的方法可以在某些参数制度下使用非私有算法实现非常紧密的性能，同时确保严格的隐私。
Non-negative matrix factorization is a popular unsupervised machine learning algorithm for extracting meaningful features from data which are inherently non-negative. However, such data sets may often contain privacy-sensitive user data, and therefore, we may need to take necessary steps to ensure the privacy of the users while analyzing the data. In this work, we focus on developing a Non-negative matrix factorization algorithm in the privacy-preserving framework. More specifically, we propose a novel privacy-preserving algorithm for non-negative matrix factorisation capable of operating on private data, while achieving results comparable to those of the non-private algorithm. We design the framework such that one has the control to select the degree of privacy grantee based on the utility gap. We show our proposed framework's performance in six real data sets. The experimental results show that our proposed method can achieve very close performance with the non-private algorithm under some parameter regime, while ensuring strict privacy.
