A privacy image encryption algorithm based on piecewise coupled map lattice with multi dynamic coupling coefficient

Wang, Xingyuan; Yang, Jingjing

doi:10.1016/j.ins.2021.04.013

Cited by 130 publications

(34 citation statements)

References 37 publications

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“…Chaotic maps are extremely sensitive to initial conditions, and are nonlinear, highly random and unpredictable in nature. These characteristics are used in cryptography applications in order to provide better security in the channel [ 3 , 4 ]. The main motivation of the proposed research work was to prevent multimedia threats in the communication channel.…”

Section: Introductionmentioning

confidence: 99%

Image Encryption Using a Spectrally Efficient Halton Logistics Tent (HaLT) Map and DNA Encoding for Secured Image Communication

Patel

Veeramalai

2022

Entropy

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With the advancement of technology worldwide, security is essential for online information and data. This research work proposes a novel image encryption method based on combined chaotic maps, Halton sequence, five-dimension (5D) Hyper-Chaotic System and Deoxyribonucleic Acid (DNA) encoding. Halton sequence is a known low-discrepancy sequence having uniform distribution in space for application in numerical methods. In the proposed work, we derived a new chaotic map (HaLT map) by combining chaotic maps and Halton sequence to scramble images for cryptography applications. First level scrambling was done by using the HaLT map along with a modified quantization unit. In addition, the scrambled image underwent inter- and intra-bit scrambling for enhanced security. Hash values of the original and scrambled image were used for initial conditions to generate a 5D hyper-chaotic map. Since a 5D chaotic map has complex dynamic behavior, it could be used to generate random sequences for image diffusion. Further, DNA level permutation and pixel diffusion was applied. Seven DNA operators, i.e., ADD, SUB, MUL, XOR, XNOR, Right-Shift and Left-Shift, were used for pixel diffusion. The simulation results showed that the proposed image encryption method was fast and provided better encryption compared to ‘state of the art’ techniques. Furthermore, it resisted various attacks.

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Section: Introductionmentioning

confidence: 99%

Image Encryption Using a Spectrally Efficient Halton Logistics Tent (HaLT) Map and DNA Encoding for Secured Image Communication

Patel

Veeramalai

2022

Entropy

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“…For example, a damaged medical image may affect a doctor's diagnosis and endanger the patient's life. Therefore, there are many images encryption algorithms [23,25,26] and image steganography [27,28] are proposed to ensure the security of images. In addition, to prevent the integrity of the image from being tampered with after encryption, the image recovery scheme has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Medical images lossless recovery based on POB number system and image compression

Zhang

et al. 2022

Multimed Tools Appl

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To protect the information integrity of the medical images, this paper proposes a secure lossless recovery scheme for medical images based on image block compression and permutation ordered binary (POB) number system, which includes two parts: share generation, and lossless recovery. In the shares generation stage, the region of interest (ROI) of the original medical image and the image block compression coding algorithm JPEG-LS, are firstly adopted to generate the compressed data, which can use limited storage space to place the data repeatedly. Then, after separating the bit-plane, the compressed data are executed by data reorganization and data encryption respectively on the high-plane and the low-plane. Finally, two authentication bits are extracted by 8-bit pixel of two planes to be inserted into the pixel itself, and then the 10-bit pixel is converted into an 8-bit POB value to produce two shares of ShareH and ShareL, respectively. In the lossless recovery stage, data of placing repeatedly can restore the original image. At the same time, three attacks are carried in the two shares, which contain content cropping, content exchange, and text addition. Some comparisons with other schemes present that the proposed scheme implements a better performance under some criteria. Theoretical analysis and experimental results demonstrate that the original image can be recovered losslessly even if two shares are tampered at the ration more than 50%.

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“…Among varieties of techniques to protect privacy, e.g., anonymization [10], encryption [11], data access control [12], data outsourcing [13], digital forgetting [14], and data summarization [15], differential privacy (DP) offers a promising approach to make the contribution of individual data items hardly distinguishable toward given data analyzing tasks [16]. Such a feature leads to a provable privacy guarantee with a quantitative privacy measurement called privacy budget [17], making DP the de-facto standard for privacy preservation in data analysis both in academia and industry [18,19].…”

Section: Introductionmentioning

confidence: 99%

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

Zhang¹,

Ma²,

Cao³

et al. 2022

Preprint

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Differential privacy (DP) has been the de-facto standard to preserve privacy-sensitive information in databases. Nevertheless, there lacks a clear and convincing contextualization of DP in the image database, where individual images' indistinguishable contribution to a specific analysis can be achieved and observed when DP is exerted. As a result, the privacy-accuracy trade-off due to integrating DP is insufficiently demonstrated in the context of differentiallyprivate image databases. This work aims at contextualizing DP in an image database by an explicit and intuitive demonstration of integrating conceptional differential privacy with images. To this end, we design a lightweight approach dedicated to privatizing the image database as a whole and preserving the statistical semantics of the image database to an adjustable level, while making individual images' contribution to such statistics indistinguishable. The designed approach leverages principal component analysis (PCA) to reduce the raw image with a large number of attributes to a lower-dimensional space, whereby DP is performed to decrease the DP load of calculating sensitivity attribute-by-attribute. The DP-exerted image data, which is not visible in its privatized format, is visualized through PCA inverse such that both a human and machine inspector can evaluate the privatization and quantify the privacy-accuracy trade-off in an analysis on the privatized image database. Using the devised approach, we demonstrate the contextualization of DP in images by two use cases based on deep learning models, where we show the indistinguishability of individual images induced by DP and the privatized images' retention of statistical semantics in deep learning tasks, which is elaborated by quantitative analyses on the privacy-accuracy trade-off under different privatization settings.

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A privacy image encryption algorithm based on piecewise coupled map lattice with multi dynamic coupling coefficient

Cited by 130 publications

References 37 publications

Image Encryption Using a Spectrally Efficient Halton Logistics Tent (HaLT) Map and DNA Encoding for Secured Image Communication

Image Encryption Using a Spectrally Efficient Halton Logistics Tent (HaLT) Map and DNA Encoding for Secured Image Communication

Medical images lossless recovery based on POB number system and image compression

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

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