Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

Denis, Richard St.; Madhubala, P.

doi:10.1007/s11042-021-10723-4

Cited by 54 publications

(21 citation statements)

References 59 publications

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“…In Ref. [24], a wavelet transform-based frequency-time domain method has been proposed to separate relevant signals and to compress them without losing any information. e presented framework increases efficiency; however, the achieved performance can be improved further.…”

Section: Related Work On Wbanmentioning

confidence: 99%

Designing a Healthcare-Enabled Software-Defined Wireless Body Area Network Architecture for Secure Medical Data and Efficient Diagnosis

Iqbal

Adnan

Khan

et al. 2022

Journal of Healthcare Engineering

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In the struggle against population aging, chronic diseases, and a lack of medical facilities, the emergence of Wireless Body Area Networks (WBANs) technology has ushered in optimism. WBANs use a variety of wearable and implanted biosensor nodes to constantly monitor physiological parameters such as oxygen saturation (SpO2), electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), blood pressure, respiration rate, body temperature, and pulse rate. Importantly, these vital signs are communicated to a doctor over a public network, who can diagnose ailments remotely and efficiently. Among these communications, the security and privacy of patients are the prime concerns while transferring data over an open wireless channel from biosensor nodes to a Medical Server (MS) through a Base Station (BS) for efficient medical diagnosis. Finding an effective security strategy for patients which rely on WBANs to monitor their health information is a huge challenge due to the confined nature of the WBANs environment. To tackle the above challenges, in this research, a new, efficient, and secure healthcare-enabled software-defined WBANs architecture based on Schnorr signcryption and Hyperelliptic Curve Cryptography (HECC) is suggested in which the SDN technology is integrated into WBANs. By separating the control and data planes in an efferent manner, SDN technology allows you to control and manage the network in a programmable manner. The main features of SDN, such as its programmability, flexibility, and centralized control, make it a simple and scalable network. In this research, first, a Software-Defined Wireless Body Area Networks (SD-WBANs) architecture has been designed, and then a lightweight Schnorr signcryption with Hyperelliptic Curve Cryptography (HECC) has been proposed to preserve sensitive patient data security during transmission on public networks. Moreover, a well-known Multicriteria Decision-Making (MCDM) approach known as Evaluation Based on Distance from Average Solution (EDAS) is also used to demonstrate the success of the suggested system. According to the performance analysis, the suggested approach beats previous state-of-the-art techniques in terms of computation cost, communication overhead, storage cost, and energy usage.

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Section: Related Work On Wbanmentioning

confidence: 99%

Designing a Healthcare-Enabled Software-Defined Wireless Body Area Network Architecture for Secure Medical Data and Efficient Diagnosis

Iqbal

Adnan

Khan

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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“…The integration of these technologies with network technologies gradually forms cloud computing. Cloud computing provides many services, and end users only need to extract these services without caring about the underlying computer technologies; for example, it shields the more complex issues such as data cluster management, massive data processing, and application deployment [ 2 ]. Cloud computing is a product of the convergence and development of distributed computing, utility computing, virtualization technology, web services, grid computing, and other technologies [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based English Multimedia for Universities Test Questions Modeling and Applications

Zheng

Xie

et al. 2022

Computational Intelligence and Neuroscience

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This study constructs a cloud computing-based college English multimedia test question modeling and application through an in-depth study of cloud computing and college English multimedia test questions. The emergence of cloud computing technology undoubtedly provides a new and ideal method to solve test data and paper management problems. This study analyzes the advantages of the Hadoop computing platform and MapReduce computing model and builds a distributed computing platform based on Hadoop using universities’ existing hardware and software resources. The study analyzes the advantages of the Hadoop computing platform and the MapReduce computing model. The UML model of the system is given, the system is implemented, the system is tested functionally, and the results of the analysis are given. Multimedia is the critical link to realizing the optimization of English test questions. The proper use of multimedia test questions will undoubtedly become an inevitable trend in the development of English test questions in the future, which requires every worker on the education front to continuously analyze and study the problems arising from multimedia teaching, summarize the experience of multimedia teaching, and explore new methods of multimedia teaching, so that multimedia teaching can better promote the optimization of English test questions in colleges and universities and better serve the education teaching.

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“…Additionally, they utilize prudent data storage approaches and diversity of cryptographic processes for guarantying client privacy; for example, attackers cannot read the plain text when the data are taken. Denis and Madhubala (2021) introduce a novel hybridization of data encryption module to protect the diagnoses data in medical images. The presented method is enhanced with the combination 2D‐DWT‐1L/2D‐DWT‐2L steganography with the presented hybrid encryption system.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

Alzubi

Beseiso

et al. 2021

Expert Systems

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Medical database classification problems can be considered as complex optimization problems to assure the diagnosis support precisely. In healthcare, several computer researchers have employed different deep learning (DL) approaches to enhance the classification performance. Besides, encryption is an effective way to offer secure transmission of medical data over public network. With this motivation, this paper presents new privacy‐preserving encryption with DL based medical data transmission and classification (PPEDL‐MDTC) model. The presented model derives multiple key‐based homomorphic encryption (MHE) technique with sailfish optimization (SFO), called MHE‐SFO algorithm‐based encryption process. In addition, the cross‐entropy based artificial butterfly optimization‐based feature selection technique and optimal deep neural network (ODNN) based classification is carried out. In ODNN model, the hyperparameter optimization of the DNN model is carried out utilizing the use of chemical reaction optimization (CRO) algorithm. The proposed method has been simulated utilizing Python 3.6.5 tool, which is tested using activity recognition and sleep stage dataset. A detailed comparative outcomes analysis makes sure the higher efficiency of the PPEDL‐MDTC on the state of art techniques with the detection accuracy of 0.9813 and 0.9650 on the applied activity recognition and University College Dublin Sleep Stage dataset.

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Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

Cited by 54 publications

References 59 publications

Designing a Healthcare-Enabled Software-Defined Wireless Body Area Network Architecture for Secure Medical Data and Efficient Diagnosis

Designing a Healthcare-Enabled Software-Defined Wireless Body Area Network Architecture for Secure Medical Data and Efficient Diagnosis

Cloud-Based English Multimedia for Universities Test Questions Modeling and Applications

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

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