Privacy-Preserving Tensor Analysis and Processing Models for Wireless Internet of Things
Jun Feng, Laurence Tianruo Yang, Xingang Liu, Ronghao Zhang · IEEE Wireless Communications · 2018
With the increasing popularity of cloud/fog computing and because of the limited computing capability of wireless Internet of Things (IoT) terminals, big data have been sent to clouds/ fogs for analysis and processing in wireless IoT. However, how to carry out tensor analysis and processing without compromising security and privacy is a challenge in cloud/fog-based wireless IoT applications. Tensors have emerged as powerful tools for multi-dimensional data analysis and processing in wireless IoT applications. In this article, we propose novel privacy-preserving tensor analysis and processing models in cloud/ fog computing for wireless IoT applications. More specifically, we present a privacy-preserving cyber-physical-social big data processing model in cloud, privacy-preserving tensor analysis, a processing model based on tensor train networks in cloud-fog computing, and an optimization model for privacy-preserving tensor analysis and processing. We introduce a social recommendation system in smartphones as an example demonstrating the security and effectiveness of the proposed models for wireless IoT.