An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model

Veeramakali, T.; Siva, R.; Sivakumar, Bellie; Mahesh, P. C. Senthil; Krishnaraj, N.

doi:10.1007/s11227-021-03637-3

Cited by 61 publications

(26 citation statements)

References 15 publications

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“…The performance of the proposed method has been compared with the different conventional methods such as Signcryption + (Adaptive Elephant Herd Optimization) AEHO [35], OHE [29] and ODLSB [31] in terms of PSNR, RMSE, MSE, Encryption time, Decryption time, key braking time, key size. The PSNR value obtained from the proposed method is compared with the conventional methods are demonstrated in Fig.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Additionally, blockchain technology is employed to ensure safe data transmission to the cloud server, which detects sickness presence using a residual network (ResNet)-based categorization algorithm. Veeramakali et al [29] have created a novel cryptographic-based IoT security authentication approach for blockchain security. The crucial data associated with the Internet of Things is protected in this study through the use of high-reliability Optimal Homomorphic Encryption (OHE).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

Miriam¹,

Dahiya²,

nitin³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

Miriam¹,

Dahiya²,

nitin³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Prediction models were trained with thesingle-layer perceptron learning method. An optimal deep-learning-based secure blockchain (ODLSB)-enabled intelligent IoT model for healthcare diagnosis waspresented in [17]. This model involved 3 mainprocess: secured transaction, the encryption of hash values, and medical diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

Kathamuthu¹,

Annadurai²,

Iruthayanathan³

et al. 2022

Electronics

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The healthcare industry is being transformed by the Internet of Things (IoT), as it provides wide connectivity among physicians, medical devices, clinical and nursing staff, and patients to simplify the task of real-time monitoring. As the network is vast and heterogeneous, opportunities and challenges are presented in gathering and sharing information. Focusing on patient information such as health status, medical devices used by such patients must be protected to ensure safety and privacy. Healthcare information is confidentially shared among experts for analyzing healthcare and to provide treatment on time for patients. Cryptographic and biometric systems are widely used, including deep-learning (DL) techniques to authenticate and detect anomalies, andprovide security for medical systems. As sensors in the network are energy-restricted devices, security and efficiency must be balanced, which is the most important concept to be considered while deploying a security system based on deep-learning approaches. Hence, in this work, an innovative framework, the deep Q-learning-based neural network with privacy preservation method (DQ-NNPP), was designed to protect data transmission from external threats with less encryption and decryption time. This method is used to process patient data, which reduces network traffic. This process also reduces the cost and error of communication. Comparatively, the proposed model outperformed some standard approaches, such as thesecure and anonymous biometric based user authentication scheme (SAB-UAS), MSCryptoNet, and privacy-preserving disease prediction (PPDP). Specifically, the proposed method achieved accuracy of 93.74%, sensitivity of 92%, specificity of 92.1%, communication overhead of 67.08%, 58.72 ms encryption time, and 62.72 ms decryption time.

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“…Therefore, blockchain technology is an important technology needed by the Internet of Things and other industries [20]. There are many application points in the fields of health care [21], intelligent transportation [22], access control [23], and smart cities [24]. However, it cannot be ignored that the blockchain is essentially a distributed database.…”

Section: Introductionmentioning

confidence: 99%

EOS.IO blockchain data analysis

et al. 2021

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Cryptocurrency is a relatively mature application of blockchain technology. The openness of transaction records provides researchers with the opportunity to analyse and compare various cryptocurrencies. The EOS public chain based on EOS. IO supports millions of transactions per second, with billions of transactions, and provides data analysts with a large quantity of analysable transaction data. Combined with the Ethereum platform data of the same period, this paper focuses on the transaction data in the EOS.IO blockchain and analyses the data in the Ethereum and EOS.IO chains from a complex network perspective. By constructing cumulative networks and time-slicing methods, constructing transaction networks of different scales, and dynamically analysing the laws of transaction network changes over time, we find that many transactions, such as transaction volume and transaction relationships, exhibit heavy-tail characteristics and conform to a power-law distribution. In particular, with the change in time and the growth in network scale, the power-law distribution is time-invariant. Our research can verify and predict the progress of blockchain development. Through graph analysis, we also obtained some other observations and discovered some interesting mathematical characteristics that explain the actual interactions that occurred on the blockchain.

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An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model

Cited by 61 publications

References 15 publications

Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

Deep Q-Learning-Based Neural Network with Privacy Preservation Method for Secure Data Transmission in Internet of Things (IoT) Healthcare Application

EOS.IO blockchain data analysis

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