With the promulgation of data protection laws (e.g., GDPR in 2018), privacy preservation has become a general agreement in applications where cross-domain sensitive data are utilized. Out of many privacy-preserving techniques, federated learning (FL) has received much attention as a bridge for secure data connection and cooperation. Although FL's research works have surged, some classical data modeling methods are not well accommodated in FL. In this paper, we propose the first masking-based federated singular vector decomposition method, called FedSVD. FedSVD protects the raw data through a singular value invariance mask, which can be further removed from the SVD results. Compared with prior privacy-preserving SVD solutions, FedSVD has lossless results, high confidentiality, and excellent scalability. We provide privacy proof showing that FedSVD has guaranteed data confidentiality. Empirical experiments on real-life datasets and synthetic data have verified the effectiveness of our method. The reconstruction error of FedSVD is around 0.000001% of the raw data, validating the lossless property of FedSVD. The scalability of FedSVD is nearly the same as the standalone SVD algorithm. Hence, FedSVD can bring privacy protection almost without sacrificing any computation time or communication overhead.
翻译:随着数据保护法的颁布(例如2018年的GDPR),隐私保护已成为使用跨域敏感数据的应用程序的普遍协议。在许多隐私保护技术中,联邦学习(FL)作为安全数据连接与合作的桥梁受到极大关注。尽管FL的研究工作激增,一些经典数据模型方法在FL没有很好地适应。在本文中,我们提议第一种基于遮蔽的、基于掩蔽的联邦化的单一矢量分解法,称为FedSVD。FedSVD通过一个单一的变换掩码来保护原始数据,这可以从SVD结果中进一步去除。与先前的保密SVD解决方案相比,FedSVD有无损、高度保密和极好的可缩缩放性。我们提供隐私证明FDSVD保证数据保密性。关于真实生命数据集和合成数据的实验证实了我们的方法的有效性。FDSVD的重建错误是原始数据的0.0001%%左右的值,可以进一步删除SVD的原始数据。FD的保密性能几乎使FD的自动计算成为SV的无损性。