A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

He, Qi; Yu, Shui; Xu, Huifang; Liu, Jia; Huang, Dongmei; Li, Guohua; Xu, Fuqiang; Du, Yanling

doi:10.32604/cmc.2019.03703

Cited by 22 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this scheme still relies on the traditional public-key system, and how to manage the key remains to be conducted. To solve the problem of secure storage of remotesensing images in an open cloud environment, Qi et al [23] propose an image-sharing scheme by weighting cloud service providers. The scheme achieves secure and efficient storage of large-scale images through the Chinese Remainder Theorem.…”

Section: Intelligence Technology For Remote Sensing Imagementioning

confidence: 99%

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

Zhang

Shafiq

Wang

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

With the arrival of new data acquisition platforms derived from the Internet of Things (IoT), this paper goes beyond the understanding of traditional remote sensing technologies. Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation. However, due to the complex architecture of IoT and the lack of a unified security protection mechanism, devices in remote sensing are vulnerable to privacy leaks when sharing data. It is necessary to design a security scheme suitable for computation‐limited devices in IoT, since traditional encryption methods are based on computational complexity. Visual Cryptography (VC) is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images. The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT. In this study, the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved. By diffusing the error between the encryption block and the original block to adjacent blocks, the degradation of quality in recovery images is mitigated. By fine‐tuning the pre‐trained model from large‐scale datasets, we improve the recognition performance of small encryption datasets for remote sensing images. The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

show abstract

Section: Intelligence Technology For Remote Sensing Imagementioning

confidence: 99%

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

Zhang

Shafiq

Wang

et al. 2023

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…However, this scheme still relies on the traditional public-key system, and how to manage the key remains to be determined. To solve the problem of secure remote sensing image storage in an open cloud environment, Qi et al [20] proposed an image-sharing scheme of weighting cloud service providers. The scheme achieves the secure and efficient storage of large-scale images through the Chinese Remainder Theorem.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

“…In addition, the ML algorithms were used for the selection of hyperspectral bands in this paper. The most common tasks in ML research are regression [56], classification [57][58][59], clustering [60], and dimensionality reduction [61,62]. ML algorithms are widely used in research fields including plant diseases [63], disease diagnosis [57], and remote sensing image processing [64][65][66].…”

Section: Plos Onementioning

confidence: 99%

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

et al. 2021

View full text Add to dashboard Cite

Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.

show abstract

A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

Cited by 22 publications

References 9 publications

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

Contact Info

Product

Resources

About