Secure Cloud-Aided Object Recognition on Hyperspectral Remote Sensing Images

Gao, Peng; Zhang, Hanlin; Yu, Jia; Lin, Jie; Wang, Xiaopeng; Yang, Ming; Kong, Fanyu

doi:10.1109/jiot.2020.3030813

Cited by 15 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, there is a lot of related research in this area [34]- [36]. Here, we use the proposed properties of most security outsourcing needs to have by Gao et al [44], these properties are:…”

Section: B Secure Outsourcing Computationmentioning

confidence: 99%

Secure Cloud-Aided Approximate Nearest Neighbor Search on High-Dimensional Data

Liu,

Yinchai,

Wei

et al. 2023

IEEE Access

View full text Add to dashboard Cite

As one fundamental data-mining problem, ANN (approximate nearest neighbor search) is widely used in many industries including computer vision, information retrieval, and recommendation system. LSH (Local sensitive hashing) is one of the most popular hash-based approaches to solve ANN problems. However, the efficiency of operating LSH needs to be improved, as the operations of LSH often involve resource-consuming matrix operations and high-dimensional large-scale datasets. Meanwhile, for resource-constrained devices, this problem becomes more serious. One way to handle this problem is to outsource the heavy computing of high-dimensional large-scale data to cloud servers. However, when a cloud server responsible for computing tasks is untrustworthy, some security issues may arise. In this study, we proposed a cloud server-aided LSH scheme and the application model. This scheme can perform the LSH efficiently with the help of a cloud server and guarantee the privacy of the client's information. And, in order to identify the improper behavior of the cloud server, we also provide a verification method to check the results returned from the cloud server. Meanwhile, for the implementation of this scheme on resource-constrained devices, we proposed a model for the real application of this scheme. To verify the efficiency and correctness of the proposed scheme, theoretical analysis and experiments are conducted. The results of experiments and theoretical analysis indicate that the proposed scheme is correct, verifiable, secure and efficient.

show abstract

Section: B Secure Outsourcing Computationmentioning

confidence: 99%

Secure Cloud-Aided Approximate Nearest Neighbor Search on High-Dimensional Data

Liu,

Yinchai,

Wei

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This method can secure transmitted remote sensing data but cannot restrict accessible content by roles to achieve granularity control. Gao et al [22] proposed to encrypt remote sensing images in distribution for cloud-based object recognition. This algorithm can encrypt the matrices of images based on the eigenvalue decomposition, but it has the same drawback with the scheme [21] to be unable to support granularity control.…”

Section: Related Workmentioning

confidence: 99%

Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation

Zhang

Huang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Facilitated by the Internet of Things (IoT) and diverse IoT devices, remote sensing data are evolving into the multimedia era with an expanding data scale. Massive remote sensing data are collected by IoT devices to monitor environments and human activities. Because IoT devices are involved in the data collection, there are probably private data contained in the collected remote sensing data, such as the device owner information and the precise location. Therefore, when data analysts, researchers, and other stakeholders require remote sensing data from numerous IoT devices for different analyses and investigations, how to distribute massive remote sensing data efficiently and regulate different people to view different parts of the distributed remote sensing data is a challenge to be addressed. Many general solutions rely on granular access control for content distribution but do not consider the low computational efficiency caused by the huge file size of the remote sensing data or certain IoT devices only have a constrained computational performance. Therefore, we propose a new granular content distribution scheme, which is more lightweight and practical for the distribution of multimedia remote sensing data with the consideration of the large data size to avoid complicated operations to the data. Furthermore, a dual data integrity check (hash summary and watermark) designed in our scheme can detect tampering or forgery from encrypted remote sensing data before decrypting it and validate it again after decryption. The security analyses and experimental results manifest that our new scheme can maintain high computational efficiency and block tampering and forgery during the granular content distribution for IoT remote sensing data.

show abstract

“…Underwater images without any preprocessing can hardly be applied for further applications, such as image detection [11][12][13] and segmentation [14][15]. There have been many explorations on improving visibility, enhancing contrast and recovering genuine color for underwater degraded images.…”

Section: B Related Workmentioning

confidence: 99%

A Hybrid Framework for Underwater Image Enhancement

Hou

Tan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Underwater captured images often suffer from poor visibility caused by two major degradations: scattering and absorption. In this paper, we propose a hybrid framework for underwater image enhancement, which unifies underwater white balance and variational contrast and saturation enhancement. In our framework, the improved underwater white balance (UWB) algorithm is integrated with histogram stretching, aiming to better compensate the attenuation difference along the propagation path and remove undesired color castings. In addition, a variational contrast and saturation enhancement (VCSE) model is developed based on the enhanced result obtained from UWB. The advantages of VCSE model lie in the improvements of contrast and saturation as well as the elimination of hazy appearance induced by scattering. Moreover, we design a fast Gaussian pyramid-based algorithm to speed up the solving of VCSE model. The improvements achieved by our method include the more effective in color correction, haze removal and detail clarification. Extensive qualitative and quantitative assessments demonstrate that the proposed approach obtains high quality outcomes, which outperforms several state-ofthe-art methods. Application tests further verify the effectiveness and broad application prospects of our proposed method.

show abstract

Secure Cloud-Aided Object Recognition on Hyperspectral Remote Sensing Images

Cited by 15 publications

References 39 publications

Secure Cloud-Aided Approximate Nearest Neighbor Search on High-Dimensional Data

Secure Cloud-Aided Approximate Nearest Neighbor Search on High-Dimensional Data

Granular Content Distribution for IoT Remote Sensing Data Supporting Privacy Preservation

A Hybrid Framework for Underwater Image Enhancement

Contact Info

Product

Resources

About