Differentially Private Tensor Train Decomposition in Edge-Cloud Computing for SDN-Based Internet of Things

Abstract

With the advent of the 5G era, the Internet of Things (IoT) will flourish in the future. Millions of IoT devices will be connected by 5G networks, which will bring great challenges to network management. Software-defined network (SDN) is a novel solution for managing a large number of IoT devices over the network. In order to solve the problems of secure data analysis in SDN-based IoT, a differentially private tensor computing model (DPTCM) is proposed in this article. Our approach utilizes tensor to model and analyze the SDN-based IoT big data, and an algorithm named differentially private tensor train decomposition (DPTTD) is proposed to achieve secure computing in SDN-based IoT. The algorithm can make full use of the flexible computing power of edge-cloud computing so as to realize collaborative computing between edges, cloud, and the third party. By separating the calculation process of the private data and nonprivate data, the algorithm implements the localized calculation and preservation of the original data which can protect the data security from the source. Meanwhile, we use the differential privacy technology to protect the privacy of data transmitted to the cloud. Finally, we prove that the algorithm satisfies $\varepsilon $ -differential privacy. In the experiments, we verify our model on two real-world data sets. The experimental results show that differential privacy has a little side effect on prediction results, and our model has good performance in data prediction.