Optimized and federated soft-impute for privacy-preserving tensor completion in cyber-physical-social systems

In recent years, an increasing amount of computing resources is distributed in social spaces. Each smart device or individual computing terminal in physical spaces is connected to cyber spaces, and they also have a relationship in social spaces. Cyber-physical-social systems (CPSSs) integrate physical space, cyber space, and social space. CPSSs need to process largescale multi-source data, which are used to recommend, predict, cluster, etc. In a CPSS, data are distributed in different locations, where each local part preserves their own data. For example, many individual medical research groups have their own health disorders data held in a smart healthcare system. A credible personalized privacy model in a CPSS mainly contains a user privacy framework, which aims to achieve the balance among service usability, user manage usability, and privacy protection [47]. Tensors are widely and effectively used for big data analysis in a CPSS [43]. Tensor decomposition and tensor completion Cyber-physical-social systems (CPSSs) handle large-scale multi-source data in different application areas, and the collected data usually contain personal private information and uncompleted information, which are typically distributed in different locations. Tensor completion has been widely used for recovering the missing entries in scale multidimensional data, and has proven to be an effective method. Privacy-preserving tensor completion in CPSSs, however, faces challenging issues, such as scalability, scatter, and security. In this paper, we propose a privacy-preserving tensor completion method that uses the optimized federated soft-impute algorithm with a differentially private guarantee. Moreover, we theoretically analyzed the privacy guarantee and utility guarantee. We evaluated the proposed algorithms on both synthetic data and real-world data. The results show that our algorithm performed better and provided strong privacy protection under a federated learning framework. Our method significantly saved space and time for privacy-preserving tensor completion in a CPSS. (c) 2021 Elsevier Inc. All rights reserved.