Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey - Zhang, Qiao; Xin, Chunsheng; Wu, Hongyi (IEEE internet of things journal, 2021)
Deep learning (DL) has demonstrated superior success in various of applications, such as image classification, speech recognition, and anomalous detection. The unprecedented performance gain of DL largely depends on tremendous training data, high-performance computation resources, and well-designed model structures. However, privacy concerns raise from such necessities. First, as the training data are usually distributed among multiple parties, directly exposing and collecting such large amount of data could violate the laws especially for private information, such as personal identities, medical records, and financial profiles. Second, locally deploying advantageous computation resources is costly for individual party having partial data. Third, direct release of well-trained model parameters threatens the information about training data or the intellectual property of model owners. Therefore, individual party prefers outsourcing computation (data) in a secure way to powerful cloud servers such as Microsoft Azure, and how to enable the cloud servers to perform DL algorithms without revealing data owners' private information and model owners' valuable parameters is emerging as an urgent task, which is termed as privacy-preserving (outsourcing) DL. In this article, we review the state-of-the-art researches in privacy-preserving DL based on multiparty secure computation with data encryption and summarize these techniques in both training phase and inference phase. Specifically, we categorize the techniques with respect to the linear and nonlinear computations, which are the two basic building blocks in DL. Following a comprehensive overview of each research scheme, we present primary technical hurdles needed to be addressed and discuss several promising directions for future research.