Zero knowledge proofs based authenticated key agreement protocol for sustainable healthcare

Gaba, Gurjot Singh; Hedabou, Mustapha; Kumar, Pardeep; Braeken, An; Liyanage, Madhusanka; Alazab, Mamoun

doi:10.1016/j.scs.2022.103766

Cited by 46 publications

(17 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can have significant impact in domains of image classification, image processing and image transmission over networks [41,42]. It will also have cyber-security impact in the domains of health care [43], cloud computing [44,45], image transmission using optical fibre [46][47][48] and deep learning [49].…”

Section: Impact Of Cd-ganmentioning

confidence: 99%

Chained Dual-Generative Adversarial Network: A Generalized Defense Against Adversarial Attacks

Singh¹,

Awasthi²,

Garg³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Neural networks play a significant role in the field of image classification. When an input image is modified by adversarial attacks, the changes are imperceptible to the human eye, but it still leads to misclassification of the images. Researchers have demonstrated these attacks to make production self-driving cars misclassify Stop Road signs as 45 Miles Per Hour (MPH) road signs and a turtle being misclassified as AK47. Three primary types of defense approaches exist which can safeguard against such attacks i.e., Gradient Masking, Robust Optimization, and Adversarial Example Detection. Very few approaches use Generative Adversarial Networks (GAN) for Defense against Adversarial Attacks. In this paper, we create a new approach to defend against adversarial attacks, dubbed Chained Dual-Generative Adversarial Network (CD-GAN) that tackles the defense against adversarial attacks by minimizing the perturbations of the adversarial image using iterative oversampling and undersampling using GANs. CD-GAN is created using two GANs, i.e., CDGAN's Sub-Resolution GAN and CDGAN's Super-Resolution GAN. The first is CDGAN's Sub-Resolution GAN which takes the original resolution input image and oversamples it to generate a lower resolution neutralized image. The second is CDGAN's Super-Resolution GAN which takes the output of the CDGAN's Sub-Resolution and undersamples, it to generate the higher resolution image which removes any remaining perturbations. Chained Dual GAN is formed by chaining these two GANs together. Both of these GANs are trained independently. CDGAN's Sub-Resolution GAN is trained using higher resolution adversarial images as inputs and lower resolution neutralized images as output image examples. Hence, this GAN downscales the image while removing adversarial attack noise. CDGAN's Super-Resolution GAN is trained using lower resolution adversarial images as inputs and higher resolution neutralized images as output images. Because of this, it acts as an Upscaling GAN while removing the adversarial attak noise. Furthermore, CD-GAN has a modular design such that it can be prefixed to any existing classifier without any retraining or extra effort, and 2542 CMC, 2023, vol.74, no.2 can defend any classifier model against adversarial attack. In this way, it is a Generalized Defense against adversarial attacks, capable of defending any classifier model against any attacks. This enables the user to directly integrate CD-GAN with an existing production deployed classifier smoothly. CD-GAN iteratively removes the adversarial noise using a multi-step approach in a modular approach. It performs comparably to the state of the arts with mean accuracy of 33.67 while using minimal compute resources in training.

show abstract

Section: Impact Of Cd-ganmentioning

confidence: 99%

Chained Dual-Generative Adversarial Network: A Generalized Defense Against Adversarial Attacks

Singh¹,

Awasthi²,

Garg³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…For minor health-care institutions [18], detailed research [19] on brain tumor analysis has been offered. The author conducted a poll that identified a number of issues with the methodology.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

Shelatkar

Garg

Shorfuzzaman

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can diagnose brain tumors by employing magnetic resonance imaging (MRI) as a supporting technology. We propose transfer learning approaches for a deep learning model to detect malignant tumors, such as glioblastoma, using MRI scans in this study. This paper presents a deep learning-based approach for brain tumor identification and classification using the state-of-the-art object detection framework YOLO (You Only Look Once). The YOLOv5 is a novel object detection deep learning technique that requires limited computational architecture than its competing models. The study used the Brats 2021 dataset from the RSNA-MICCAI brain tumor radio genomic classification. The dataset has images annotated from RSNA-MICCAI brain tumor radio genomic competition dataset using the make sense an AI online tool for labeling dataset. The preprocessed data is then divided into testing and training for the model. The YOLOv5 model provides a precision of 88 percent. Finally, our model is tested across the whole dataset, and it is concluded that it is able to detect brain tumors successfully.

show abstract

“…Computational and Mathematical Methods in Medicine security and privacy in cloud computing can be implemented to design prototype for cloud layer devices [15][16][17]. and the demonstration of space time coding for MDL mitigation and capacity enhancement of FTS gives right direction for transmission criteria [18][19][20].…”

Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

show abstract

Zero knowledge proofs based authenticated key agreement protocol for sustainable healthcare

Cited by 46 publications

References 46 publications

Chained Dual-Generative Adversarial Network: A Generalized Defense Against Adversarial Attacks

Chained Dual-Generative Adversarial Network: A Generalized Defense Against Adversarial Attacks

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Contact Info

Product

Resources

About