Encryption of ciphertext data in Internet of Things based on HECRT key management

Wang, Meini; Zhao, Wei; Liu, Ying

doi:10.1504/ijaacs.2022.123460

Cited by 2 publications

(1 citation statement)

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“…Although it has high security and retrieval accuracy, the calculation cost is too high. PPCBIR scheme [17] extracts features from images and builds a security index, and then, traditional encryption protects images [18]. Although it has high security and retrieval accuracy, users are required to extract features, build, and update indexes in real time, but mobile devices cannot bear the computational complexity under large-scale and dynamic image updating.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Image Retrieval Based on Disordered Local Histograms and Vision Transformer in Cloud Computing

Wang,

Qin,

Xiang

et al. 2023

International Journal of Intelligent Systems

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Frequent data breaches in the cloud environment have seriously affected cloud subscribers and providers. Privacy-preserving image retrieval methods can improve the security of cloud image retrieval; however, existing methods have limited accuracy on dynamically updated image databases and mobile lightweight devices. In this study, we propose a privacy-preserving image retrieval method based on disordered local histograms and vision transformer in cloud computing, by designing a multiple encryption method and transformer-based feature model to better mine the local feature value of encrypted images. Specifically, the user performs different value substitution, position substitution, and color substitution on the subblocks of the image to protect the image information. The cloud server extracts the unordered local histogram from the encrypted image and generates retrievable features using transformer. Experiments show that compared with similar CNN schemes, the retrieval accuracy of this method is improved by 8.5%, and the retrieval efficiency is improved by 54.8%.

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Section: Introductionmentioning

confidence: 99%