Cloud-Based Healthcare Portal in Virtual Private Cloud

Mahaveerakannan, R.; Dhas, C. Suresh Gnana; Devi, R. Rama

doi:10.1007/978-981-15-0146-3_103

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Here, the sender generally encrypts the patient information with P k u a receiver, although the receiver decrypts the patient information P k r . Moreover, the cloud system creates the public key (P k u ) based on Equation (7),…”

Section: Beginmentioning

confidence: 99%

“…These crypto algorithms are one of the schemes known as public cloud, introduced for the medical cloud environment to secure medical data 6 . Nowadays, all the applications and services are digitalized, so medical facilities are also readily available through the internet anywhere at any time 7 . However, security is the primary concern for carrying on medical services through the wireless environment 8 .…”

Section: Introductionmentioning

confidence: 99%

“…6 Nowadays, all the applications and services are digitalized, so medical facilities are also readily available through the internet anywhere at any time. 7 However, security is the primary concern for carrying on medical services through the wireless environment. 8 This key generation concept can avoid Symbols and Abbreviations: DL, Deep learning; ML, Machine learning; IoT, Internet of Things; ABIDE, African buffalo-based International Data Encryption; CS, Cloud server; MRI, Magnetic resonance imaging; ABO, African buffalo optimization; IDE, International data encryption; SA-DS, Secure Authentication based data sharing; MWO, Modified whale optimization; a m n , General symptoms for each disease; n, Cloud database parameter; d p , The particular disease of the affected patient; z (1) 1 , z (2) 2 , z (3) 3 , z (4) 4 , Encryption process; h ′ , Hash function of the public cloud system; h d , Healthcare data card of the patient; r ′ , Randomly selected value; S ′ n , Medical data record; 𝛽 ′ , Actual healthcare information; 𝛼 ′ , Collected healthcare information; a ′ d , c ′ d , Healthcare information from the cloud database; P k+1 , Exploitation performance; e k , User detail; lg 1 F I G U R E 1 Public cryptosystem the third person in the cloud environment by locking the user details.…”

Section: Introductionmentioning

confidence: 99%

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An optimized novel public cloud system to secure the medical record from third parties

Singh

Jha

2022

Concurrency and Computation

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Nowadays, modern technologies are growing rapidly by advancing their application day by day. Moreover, the Internet of Things (IoT) is well utilized in many applications for sensing and information gathering purposes. Considering this, medical applications have used the advances of IoT facilities in sensing the disease rate in humans. After sensing, the sensed data is stored in the cloud system, so securing the cloud system is the more required task to maintain the privacy of the patient's data. But, in front of malicious activities, securing the data is not easy. So, the present research has proposed a novel African buffalo‐based International Data Encryption (ABIDE) public cloud system to secure the medical database during the data transmission. The developed technique is utilized in a public cloud system. The main objective of this research is to secure medical data during transmission. Moreover, the implementation of the proposed ABIDE algorithm is done in the MATLAB environment. Consequently, the performance of the developed technique is compared with existing methods in terms of resource usage, encryption time, decryption time and so forth. Therefore, the proposed model has attained the finest confidential rate of 98.98%, throughput 97.86% and less processing time of 4 ms.

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Section: Beginmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An optimized novel public cloud system to secure the medical record from third parties

Singh

Jha

2022

Concurrency and Computation

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Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

Bhalaji¹

2021

JSCP

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In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

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Cloud-Based Healthcare Portal in Virtual Private Cloud

Cited by 2 publications

References 9 publications

An optimized novel public cloud system to secure the medical record from third parties

An optimized novel public cloud system to secure the medical record from third parties

Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

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