Secure Cloud-Aided Object Recognition on Hyperspectral Remote Sensing Images

Object recognition of hyperspectral remote sensing images based on machine learning is widely applied in many industries. However, the efficiency of the training and recognizing process of object recognition on hyperspectral remote sensing images is a critical issue since it involves complex matrix operations and large scale training data sets, especially for resource-constrained devices. One solution is to outsource the heavy workload of object recognition on hyperspectral remote sensing images to a cloud server. Nonetheless, it may bring some security problems when the cloud server is untrustworthy. Therefore, how to enable resource-constrained devices to securely and efficiently accomplish the training and recognizing process of object recognition on hyperspectral remote sensing images is of significant importance. In this article, we propose a secure and efficient scheme to outsource the object recognition on hyperspectral remote sensing images to the untrustworthy cloud server. The proposed scheme can protect the privacy of the computation input and output. Also, we develop an effective verification approach in our scheme that can detect the misbehavior of cloud server with the optimal probability 1. The theoretical analysis and experimental results indicate that our proposed scheme is secure and efficient.

Automated summary

This article addresses the issue of low efficiency in training and recognizing object recognition on hyperspectral remote sensing images on resource-constrained devices, proposing a secure and efficient scheme to outsource the task to untrustworthy cloud servers. The scheme protects the privacy of computation input and output and effectively detects misbehavior of the cloud server with optimal probability 1, ensuring security and efficiency.