CIE-LSCP: color image encryption scheme based on the lifting scheme and cross-component permutation

Chen, Xiuhui; Gong, Mengxin; Gan, Zhihua; Yang, Lu; Chai, Xiuli; He, Xin

doi:10.1007/s40747-022-00835-1

“…Random numbers and pseudo random numbers are widely used in the field of cryptography [30]. The chaotic sequence generated by the chaotic system and the encrypted cryptographic image data stream should be sufficiently random.…”

Section: Nist Analysismentioning

confidence: 99%

Fully chaotic medical image encryption scheme based on dynamic DNA and block rotation

Xiong

¹

,

Ji'e

²

,

Wang

³

et al. 2023

Phys. Scr.

4

0

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As an important application of image encryption, digital medical image encryption plays an important role in the field of medical health and privacy protection. This paper put forwards a fully chaotic and strongly plaintext associated image encryption framework based on an improved chaotic system, block rotation and DNA computing. The algorithm generates multiple chaotic sequences by using differentone-dimensional seed chaotic maps under the sine transform framework for subsequent block rotation, DNA dynamic encoding and decoding, generating key images for DNA XOR calculation. Simulation experiments and comparative analysis have shown thatthis algorithm can achieve fantastic encryption performance, resist various attacks, and have higher security levels and good generalization performance.

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“…Image Chunk Merging: After encryption, the feature and non-feature fragments are merged into block groups [34]. The order of these block groups reflects the order of the original images, with each block group corresponding to an original image.…”

Section: Chunks Merge and Storagementioning

confidence: 99%

Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network

Jia,

Cai,

Yang

et al. 2023

Preprint

0

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Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.

show abstract

“…1. Image Chunk Merging After encryption, the feature and non-feature fragments are merged into block groups [45]. The order of these block groups reflects the order of the original images, with each block group corresponding to an original image.…”

Section: Chunks Merge and Storagementioning

confidence: 99%

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

Jia,

Cai,

Yang

et al. 2023

J Cloud Comp

1

0

View full text Add to dashboard Cite

Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.

show abstract

CIE-LSCP: color image encryption scheme based on the lifting scheme and cross-component permutation

Cited by 14 publications

References 42 publications

Fully chaotic medical image encryption scheme based on dynamic DNA and block rotation

Fully chaotic medical image encryption scheme based on dynamic DNA and block rotation

Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network

Efficient and privacy-preserving image classification using homomorphic encryption and chunk-based convolutional neural network

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