Perceptual Encryption-based Privacy-Preserving Deep Learning for Medical Image Analysis

Ahmad, Ijaz; Shin, Seokjoo

doi:10.1109/icoin56518.2023.10048970

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…In similar work presented in [63], the authors used an Extended CPE method for face recognition in a color image dataset. Besides face recognition tasks, CPE-based privacy-preserving image classification has been performed in [49,[64][65][66]. Specifically, in [64], the authors implemented an isotropic network such as vision transformers with the Color CPE scheme for natural image classification.…”

Section: Cpe Scheme Applicationsmentioning

confidence: 99%

“…In [65], the authors implemented four different extensions of IIB-CPE and analyzed their effect on a CNN model's accuracy. The same authors implemented a CNN-based model with a IIB-CPE scheme for natural image classification in [49] and for COVID-19 diagnosis in chest X-ray images in [66].…”

Section: Cpe Scheme Applicationsmentioning

confidence: 99%

“…Similarly, in privacy-preserving applications, the use of different keys may not achieve the desired output. For example, with the recent popularity of CPE-based PPML applications (as in [49,[64][65][66]), careful consideration should be given to how the cipher images are generated and whether the use of different keys will affect the model performance.…”

Section: Encryption Perspectivementioning

confidence: 99%

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Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Ahmad

Choi

Shin

2023

Sensors

Self Cite

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Perceptual encryption (PE) hides the identifiable information of an image in such a way that its intrinsic characteristics remain intact. This recognizable perceptual quality can be used to enable computation in the encryption domain. A class of PE algorithms based on block-level processing has recently gained popularity for their ability to generate JPEG-compressible cipher images. A tradeoff in these methods, however, is between the security efficiency and compression savings due to the chosen block size. Several methods (such as the processing of each color component independently, image representation, and sub-block-level processing) have been proposed to effectively manage this tradeoff. The current study adapts these assorted practices into a uniform framework to provide a fair comparison of their results. Specifically, their compression quality is investigated under various design parameters, such as the choice of colorspace, image representation, chroma subsampling, quantization tables, and block size. Our analyses have shown that at best the PE methods introduce a decrease of 6% and 3% in the JPEG compression performance with and without chroma subsampling, respectively. Additionally, their encryption quality is quantified in terms of several statistical analyses. The simulation results show that block-based PE methods exhibit several favorable properties for the encryption-then-compression schemes. Nonetheless, to avoid any pitfalls, their principal design should be carefully considered in the context of the applications for which we outlined possible future research directions.

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Section: Cpe Scheme Applicationsmentioning

confidence: 99%

Section: Cpe Scheme Applicationsmentioning

confidence: 99%

Section: Encryption Perspectivementioning

confidence: 99%

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Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Ahmad

Choi

Shin

2023

Sensors

Self Cite

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“…Notably, the NPT is employed in the encryption scheme of JPEG images, featuring prominently in various proposals [3][4][5][6][7]. In all of these instances, the NPT operates at the pixel block level [8] and applies to both color and grayscale images [9,10]. Moreover, the NPT finds applications beyond JPEG image encryption, extending its utility to hybrid methods like compressed sensing.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of the Negative–Positive Transformation in Image Encryption

Cardona-López,

Chimal-Eguía,

Silva-García

et al. 2024

Mathematics

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The negative–positive transformation (NPT) is a widely employed technique for encrypting images on pixel blocks, commonly integrated into cryptosystems compatible with compression algorithms. The existing literature on NPT analysis can be categorized into two types: theoretical analyses with results that apply to any image, primarily focused on compression compatibility, and numerical analyses that report empirical results from specific images, some without explaining the causes of the security results, while others are only related to the compression performance. Consequently, there is a significant gap in understanding the implications of applying the NPT for data protection. For that reason, this paper conducts a theoretical statistical analysis, presenting, demonstrating, and verifying six theorems to understand the security contributions of NPT. Two theorems examine the shape of the image histogram and the scatter plot of adjacent pixels after the NPT application. The subsequent four theorems explore the influence of NPT on the mean, variance, covariance, and correlation within each pixel block. The findings indicate that the NPT generates images with symmetrical histograms, the correlation of pixel blocks remains invariant, and distinct vertical and horizontal reflections manifest on the scatter plot. These theorems are verified by encrypting the Lena image with four pixel-block sizes. The histogram symmetry passed the goodness-of-fit test at a significance level of 5%, revealing consistent results. The correlation of pixel blocks remained unchanged, and the scatter plot exhibited an x-shaped pattern. Therefore, as the NPT alone does not achieve desirable encryption results, such as uniform histograms, scatter plots, and decreasing correlation, cryptosystems should complement it with additional techniques.

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Investigation of Machine Learning Approaches on Security Analysis of Cryptographic Algorithms

M.,

K.,

et al. 2024

Machine Learning and Cryptographic Solutions for Data Protection and Network Security

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The distributed denial of service (DDoS) assault was a kind of intrusion in the cloud computing environment that severely affects the end user by injecting illegitimate packets. To obtain performance, a hybrid improved wolf optimizer with asymmetric key Goldwasser cryptography (IWO-AKGC) algorithm was proposed based on combining the exploitation ability of security and exploration capability of machine learning. In addition to the selection of parameters, a proposed hybrid IWO-AKGC technique is used for weighting and bias coefficients in neural network models. This has led to an immediate improvement in communication security for the delivery of different types of data services via clouds, thanks to the proposed IWO-AKGC method. The recommended hybrid optimizer successfully addresses the drawbacks of conventional methods, such as local stagnation problems, delayed convergence problems, and local and global optimal trapping problems. Thus, secured data communication is obtained for cloud service provisioning. The proposed model proved to be a better model for DDoS intrusion detection.

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Perceptual Encryption-based Privacy-Preserving Deep Learning for Medical Image Analysis

Cited by 3 publications

References 11 publications

Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives

Statistical Analysis of the Negative–Positive Transformation in Image Encryption

Investigation of Machine Learning Approaches on Security Analysis of Cryptographic Algorithms

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