Application of Internet of Things and Blockchain in Information Security and Privacy Protection of Global Organizations

Fang, Shuya; Qing-quan, Liu; Zhang, Fengrui; Chen, Ningyan; Li, Xin

doi:10.4018/joeuc.323192

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…Sensitivity, specificity, accuracy, and kappa index for retinal blood detection were calculated as per the general formulae given in Eqs. ( 17)- (20): In the Heart Statlog dataset, the DBN model exhibited a sensitivity of 72.33%, specificity of 76.55%, accuracy of 72.03%, and a kappa index of 86%. The CNN model achieved a sensitivity of 86.95%, specificity of 83%, accuracy of 87.33%, and a kappa index of 79.86%.…”

Section: Performance Evaluationmentioning

confidence: 98%

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Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption

Ramachandraiah,

Bommagani,

Jayapal

2023

IDA

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In the rapidly evolving landscape of digital healthcare, the integration of cloud computing, Internet of Things (IoT), and advanced computational methodologies such as machine learning and artificial intelligence (AI) has significantly enhanced early disease detection, accessibility, and diagnostic scope. However, this progression has concurrently elevated concerns regarding the safeguarding of sensitive patient data. Addressing this challenge, a novel secure healthcare system employing a blockchain-based IoT framework, augmented by deep learning and biomimetic algorithms, is presented. The initial phase encompasses a blockchain-facilitated mechanism for secure data storage, authentication of users, and prognostication of health status. Subsequently, the modified Jellyfish Search Optimization (JSO) algorithm is employed for optimal feature selection from datasets. A unique health status prediction model is introduced, leveraging a Deep Convolutional Gated Recurrent Unit (DCGRU) approach. This model ingeniously combines Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) processes, where the GRU network extracts pivotal directional characteristics, and the CNN architecture discerns complex interrelationships within the data. Security of the data management system is fortified through the implementation of the twofish encryption algorithm. The efficacy of the proposed model is rigorously evaluated using standard medical datasets, including Diabetes and EEG Eyestate, employing diverse performance metrics. Experimental results demonstrate the model's superiority over existing best practices, achieving a notable accuracy of 0.884. Furthermore, comparative analyses with the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) models reveal enhanced performance metrics, with the proposed model achieving a processing time and throughput of 40 and 45.42, respectively.

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Section: Performance Evaluationmentioning

confidence: 98%

“…Fang et al [20] addressed the challenges in the blockchain-based global organization of IoT. The study begins with an analysis of the structural models of IoT and blockchain technology.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption

Ramachandraiah,

Bommagani,

Jayapal

2023

IDA

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Blockchain and FEF-Based Lightweight Anonymous Authentication Protocol for Wireless Medical Sensor Networks

Wu,

Chen,

Nie

et al. 2024

International Journal of Information Technologies and Systems Approach

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Authentication ensures the privacy of patients by enabling access control within wireless medical sensor networks. However, many schemes do not consider the resource-constrained environments, making those protocols unusable. Meanwhile, sensitive patient information continues to be stored and accessed in a central location, increasing the risk of “single points of failure.” To solve this problem, the researchers designed a lightweight anonymous authentication protocol based on blockchain technology and fuzzy extraction. First, they created a multiround session key negotiation mechanism. Then, they utilized the fuzzy extraction function to extract and recover multimodal biometric features. Decentralization was achieved through blockchain and smart contracts. Simultaneously, the researchers also provided a formal security proof by Burrows-Abadi-Needham logic. Finally, experiments using the Java Pairing-Based Cryptography Library show that this scheme outperforms the comparison schemes in terms of computational overhead and communication overhead.

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Application of Internet of Things and Blockchain in Information Security and Privacy Protection of Global Organizations

Cited by 2 publications

References 42 publications

Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption

Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption

Blockchain and FEF-Based Lightweight Anonymous Authentication Protocol for Wireless Medical Sensor Networks

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