A Lightweight Privacy-Preserving System for the Security of Remote Sensing Images on IoT.

The acquisition of massive remote sensing data makes it possible to deeply fuse remote sensing and artificial intelligence (AI). The mobility and cost advantages of new sensing platforms in the Internet of Things (IoT) make them ideal for continuous deployment rather than traditional airborne platforms. However, remote sensing devices are vulnerable to malicious attacks and privacy leaks when sharing data due to the complex architecture and heterogeneity of IoT and the lack of a unified security protection mechanism. Traditional protection methods based on public-key encryption require not only complex operations but also energy consumption, which poses new challenges for resources-limited IoT. The objective of this paper was to propose a lightweight privacy-preserving system for the security of remote-sensing images based on visual cryptography. This stacking-to-see feature of visual cryptography enables the efficient encryption of big data such as high-resolution and multi-scale remote sensing images in resource-constrained IoT. To alleviate image quality degradation in visual cryptography, we combined denoising neural networks to extract high-quality images from encrypted datasets, thus improving the recognition accuracy of loss datasets. We conducted extensive experiments, and the results verify the effectiveness of the proposed method in terms of privacy protection and classification accuracy.