Privacy-Preserving Deep Learning With Learnable Image Encryption on Medical Images

Huang, Qi-Xian; Yap, Wai Leong; Chiu, Min-Yi; Sun, Hung–Min

doi:10.1109/access.2022.3185206

Cited by 26 publications

(12 citation statements)

References 28 publications

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“…The scheme also carries out similarity matching in secured form in order to deploy efficient secrecy. Adoption of deep learning is reported in work of [Huang et al, 2022] where an encryption algorithm is formulated for securing image. The uniqueness of this study is towards developing a learnable encryption algorithm where the focus is towards maintaining privacy towards training images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Summary of Existing ApproachesAfter reviewing the existing approaches in the prior section, conclusive remarks are drawn regarding identifying significant research problems. Existing deep learning scheme over the encryption domain(Alkhelaiwi et al, 2021],Huang et al, 2022] is noted with highly iterative, demand training data and learning operations, and does not offer scalable performance for various ranges of an image.…”

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confidence: 99%

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Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

Dule¹,

R²

2023

INDJCSE

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Privacy preservation is always a potential concern while storing or transmitting images with sensitive information within them. A review of existing privacy preservation techniques shows the potential usage of deep learning, adoption of highly sophisticated encryption, lack of packetization scheme, and focus on a singular attack. Therefore, the proposed study presents a novel analytical framework for the privacy preservation of image data in the cloud environment. A novel and simplified mathematical model with layer-based operation is constructed, which facilitates appending image chunks with digital security code and encryption token using a hash to generate a hash vector of an image with a higher degree of secrecy. An extensive analysis is carried out over different datasets of images to find that the proposed scheme offers consistency in its performance and a significant balance between security and computational performance in contrast to frequently exercised privacy preservation schemes. The study outcome exhibited 59% overhead reduction, 25% enhanced signal quality, 65% better resolution, and 40% faster execution time in contrast to the existing scheme.

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

Dule¹,

R²

2023

INDJCSE

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“…The main advantage of their method is that it does not require an adaptation network to achieve classification accuracy on the encrypted images comparable to that of the plain images. On the other hand, learnable PE methods have been proposed to enable PPDL applications in [ 5 , 6 , 7 , 8 , 26 , 27 , 28 ]. In [ 5 ], they have proposed a PE method that forms a 6-channel image from an RGB image by splitting predefined blocks into upper and lower 4-bit images.…”

Section: Related Workmentioning

confidence: 99%

“…However, when using chaos theory-based image encryption algorithms, careful consideration is required to avoid weak and equivalent keys in order to resist different types of attacks [ 4 ]. Alternatively, [ 26 ] proposed to divide the encrypted image of [ 6 ] into blocks and apply three different types of filters on randomly selected blocks. Furthermore, the aforementioned methods that perform digital encryption, [ 27 , 28 ], have proposed optical image encryption methods to take advantage of parallel computing for PPDL.…”

Section: Related Workmentioning

confidence: 99%

IIB–CPE: Inter and Intra Block Processing-Based Compressible Perceptual Encryption Method for Privacy-Preserving Deep Learning

Ahmad

Shin

2022

Sensors

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Perceptual encryption (PE) of images protects visual information while retaining the intrinsic properties necessary to enable computation in the encryption domain. Block–based PE produces JPEG-compliant images with almost the same compression savings as that of the plain images. The methods represent an input color image as a pseudo grayscale image to benefit from a smaller block size. However, such representation degrades image quality and compression savings, and removes color information, which limits their applications. To solve these limitations, we proposed inter and intra block processing for compressible PE methods (IIB–CPE). The method represents an input as a color image and performs block-level inter processing and sub-block-level intra processing on it. The intra block processing results in an inside–out geometric transformation that disrupts the symmetry of an entire block thus achieves visual encryption of local details while preserving the global contents of an image. The intra block-level processing allows the use of a smaller block size, which improves encryption efficiency without compromising compression performance. Our analyses showed that IIB–CPE offers 15% bitrate savings with better image quality than the existing PE methods. In addition, we extended the scope of applications of the proposed IIB–CPE to the privacy-preserving deep learning (PPDL) domain.

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“…Conversely, there are several authors on analyzing accidents. Various image-processing approaches were advanced to get a real-time mechanism to assist the accident [ 7 ]. Crash severity methods may forecast severity that may be anticipated to occur for a crash that aids clinics in offering proper health care as soon as possible [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model

Sirisha

Chandana

2023

Sensors

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Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption (MKHE) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.

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Privacy-Preserving Deep Learning With Learnable Image Encryption on Medical Images

Cited by 26 publications

References 28 publications

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

Analytical and Integrated Framework for Privacy Presrvation of Images in Cloud Ecosystem

IIB–CPE: Inter and Intra Block Processing-Based Compressible Perceptual Encryption Method for Privacy-Preserving Deep Learning

Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model

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