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.

翻译：非消极矩阵乘数化是一种流行的、不受监督的机器学习算法,用于从固有的非消极数据中提取有意义的特征,然而,这类数据集可能往往含有对隐私敏感的用户数据,因此,我们可能需要采取必要步骤,在分析数据时确保用户的隐私。在这项工作中,我们侧重于在隐私保护框架内开发一种非消极矩阵乘数算法。更具体地说,我们提议一种新的隐私保存算法,用于非消极矩阵乘数化,这种算法能够利用私人数据运作,同时取得与非私人算法相类似的效果。我们设计的框架可以控制根据公用设备差距选择隐私受赠者的程度。我们用6套真实数据集展示了我们提议的框架的性能。实验结果显示,我们提出的方法可以在一些参数制度下实现非常接近的非私人算法的功能,同时确保严格的隐私。