A New Approach for Secure Cloud-Based Electronic Health Record and its Experimental Testbed

Jusak, Jusak; Mahmoud, S.A.; Laurens, Roy; Alsulami, Musleh; Fang, Qiang

doi:10.1109/access.2021.3138135

Cited by 7 publications

(1 citation statement)

References 25 publications

(50 reference statements)

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“…In [7], a novel non-cryptographic technique was developed to safeguard the security and integrity of electrocardiograph (ECG) data during transmission. Nevertheless, the efficiency of healthcare data transfer was not improved.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Krishnaveni R

2023

IJRITCC

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Cloud computing is a kind of distributed computing that use a vast network of interconnected resources accessible over the internet. Security is a crucial concern in cloud computing due to the fact that users save their data on the cloud for convenient access from any location and at any time. Consequently, many users are worried about safeguarding their sensitive data in an unsafe location. Therefore, cloud computing architecture requires an innovative cryptographic method that ensures the secrecy, authenticity, integrity, and non-repudiation of data transfer in the cloud. A new technique called SEMcrypt, which stands for Stochastic Gradient DEep Multilayer Neural Network based Linear Congruential Generative Cryptography, has been developed to enhance secure data transmission. SEMcrypt ensures higher data confidentiality and reduces the time required for communication between the cloud user (i.e., patient) and the server. The SEMcrypt approach has two distinct processes: categorization and secure data transport. Initially, the data is gathered from the patients and is used as input for the stochastic gradient regularised deep multilayer neural network. The deep neural network consists of one input layer, two hidden layers, and one output layer. At first, the information is gathered from the patients and sent to the input layer. Next, the patient data that has been gathered is examined in hidden layer 1 using the generalised Tikhonov regularisation function. The patient data that has been analysed is sent to hidden layer 2. The hyperbolic tangent activation function is used at that layer to classify the patient data. Subsequently, the categorised data undergoes encryption via the use of Linear Congruential Generative Goldwasser-Micali encryption, ensuring safe transfer of the data. Subsequently, the encrypted data is sent to the cloud server. The patient data is encrypted on the server side using the Linear Congruential Generative Goldwasser-Micali decryption technique to prevent unauthorised access or assaults. Consequently, the authorised recipient receives the unaltered information, which is then kept in the database for further analysis. Secured data transmission is achieved by ensuring better levels of data confidentiality and reducing the time required for the process. The experimental assessment focuses on criteria such as the time it takes to generate keys, the level of data confidentiality and integrity, the computing time, and the accuracy of categorization. The empirical findings demonstrate that our suggested SEMcrypt approach delivers efficient performance outcomes by attaining superior levels of data confidentiality and integrity within a minimal timeframe.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Krishnaveni R

2023

IJRITCC

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Securing data and preserving privacy in cloud IoT-based technologies an analysis of assessing threats and developing effective safeguard

Pathak,

Mishra,

Singh

2024

Artif Intell Rev

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Design and Implementation of New Collision Resistant Fast Hash Function Family for IoT Healthcare Blockchain Ledger

Fayez

Sayed

Othman

et al. 2022

2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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A New Approach for Secure Cloud-Based Electronic Health Record and its Experimental Testbed

Cited by 7 publications

References 25 publications

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Securing data and preserving privacy in cloud IoT-based technologies an analysis of assessing threats and developing effective safeguard

Design and Implementation of New Collision Resistant Fast Hash Function Family for IoT Healthcare Blockchain Ledger

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