Secure Image LBP Feature Extraction in Cloud-Based Smart Campus

Xia, Zhihua; Ma, Xinyuan; Shen, Zixuan; Sun, Xingming; Xiong, Naixue; Jeon, Byeungwoo

doi:10.1109/access.2018.2845456

Cited by 56 publications

(34 citation statements)

References 35 publications

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“…Although the outsourcing of data can reduce the computational and storage burden on smart campus, the privacy preserving becomes the biggest concern. Xia et al [Xia, Ma, Shen et al (2018)] propose an effective and practical privacy-preserving computation outsourcing protocol for the local binary pattern (LBP) feature over huge encrypted images.…”

Section: Related Applications Based On Fogmentioning

confidence: 99%

A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System

Bai

Liu²,

Wang

et al. 2019

Journal of Information Hiding and Privacy Protection

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Internet of Things (IoT) is an emerging paradigm involving intelligent sensor networks that incorporates embedded technology for collecting data, communicating with external environments. Recently, cloud computing together with fog computing has become an important research area of the Internet of Things because of big data processing capabilities. It is a promising technology that utilizes cloud or fog computing / architecture to improve sensor computing, storage, and communication capabilities. However, recently it has been shown that this computing/architecture may be vulnerable to various attacks because of the openness nature of the wireless network. Therefore, it becomes more and more important to ensure the security and privacy in these scenes. Encryption security and information hiding technology can provide authentication, confidentiality, integrity, anti-eavesdropping, availability and so on for these computing models or architectures. The purpose of this review is to look for original articles with novel ideas and solutions to address encryption security and information hiding technologies in cloud or fog-based Internet of Things systems. We hope this review will provide an opportunity for scientists, researchers and industry engineers to study original manuscripts and know developments in all aspects of security, privacy, trust, and covert communication issues in cloud or fog computing/architecture Internet of Things systems.

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Section: Related Applications Based On Fogmentioning

confidence: 99%

A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System

Bai

Liu²,

Wang

et al. 2019

Journal of Information Hiding and Privacy Protection

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“…As a result each image can be represented as a normalized histogram of the encrypted visual words. Xia et al [9] proposed a secure Local Binary Pattern (LBP) feature extraction method, where block and pixel permutation are used together to provide a privacy-protected LBP extraction scheme in the ciphertext domain. These schemes are efficient but the security is compromised.…”

Section: Introductionmentioning

confidence: 99%

A Large-Scale Secure Image Retrieval Method in Cloud Environment

Zhao

Gong³

2019

IEEE Access

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With the rapid development of cloud computing technology, more and more users choose to outsource image data to clouds. To protect data's confidentiality, images need to be encrypted before being outsourced to clouds, but this brings difficulties to some basic yet important data services, such as content-based image retrieval. Existing secure image retrieval methods generally have some problems such as low retrieval accuracy and low retrieval efficiency, which cannot meet requirements for large-scale image retrieval in cloud environment. In this paper, we propose a large-scale secure image retrieval method in cloud environment. The Hamming embedding algorithm is utilized to generate binary signatures of image descriptors. A frequency histogram combined with binary signatures is generated to provide a more precise representation of image features in an image and thus the retrieval accuracy is improved. Visual words are selected from the histogram by the random sampling method before the min-Hash algorithm is performed on binary signatures of selected visual words to generate a secure index. The random sampling method and min-Hash algorithm can not only ensure the security of the search index, but also greatly improve the image retrieval efficiency. This method achieves the balance among security, accuracy and efficiency of large-scale secure image retrieval in public clouds. The security analysis and experimental results show the effectiveness of the proposed method. INDEX TERMS Large-scale image retrieval, secure image retrieval, min-Hash, hamming embedding, secure inverted index.

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“…If no condensate is detected, the cooling block continues cooling. Otherwise the cooling block is controlled to maintain the mirror temperature, while using the image recognition algorithm to identify the condensate phase [Xia, Ma, Shen et al (2018)…”

Section: Dew Point Measuring Principle Introductionmentioning

confidence: 99%

Analysis and Test on Influence Factors of Dew Drop Condensation in Dew Point Hygrometer

Zhao¹,

Chen²,

Wang³

et al. 2019

Journal of Quantum Computing

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The condensation process of dew droplets is influenced by many factors. A dew point condensation image observation system was built to improve the response speed of dew point detector under different measuring conditions. The basic mechanism of dew drop condensation growth was studied and the influence of various factors on the dew drop growth rate were analyzed. And the accuracy of the influence results was verified based on the improved Hough transform circle detection. The results show that the growth rate of dew droplets is affected by ambient temperature, dew point temperature, mirror temperature and air velocity. The observed variation of the average radius of dew droplets is consistent with the theoretical calculations. The maximum radius error is less than 4 μm, the initial error is larger, and the error oscillates in the middle and late stages of condensation. The establishment of condensation mechanism is helpful to solve the problem in fast determination of dew point temperature under the cold start of dew point meter, and to improve the response speed.

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Secure Image LBP Feature Extraction in Cloud-Based Smart Campus

Cited by 56 publications

References 35 publications

A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System

A Survey on Cryptographic Security and Information Hiding Technology for Cloud or Fog-Based IoT System

A Large-Scale Secure Image Retrieval Method in Cloud Environment

Analysis and Test on Influence Factors of Dew Drop Condensation in Dew Point Hygrometer

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