A Secure Blockchain-Assisted Access Control Scheme for Smart Healthcare System in Fog Computing

Li, Jiahao; Li, Dongmei; Zhang, Xiaomei

doi:10.1109/jiot.2023.3268278

Cited by 19 publications

(5 citation statements)

References 32 publications

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“…In the context of the Internet of Vehicles (IoV) and vehicle-to-vehicle (V2V) communication and coordination, numerous studies [30][31][32][33] have explored how BIoT offers trusted communications and data exchanges, therefore contributing to safer and more efficient vehicular networks. Digital health is another sector where BIoT systems can provide significant benefits, particularly in ensuring privacy and creating tamper-proof digital health records [34][35][36][37][38]. These applications demonstrate the versatility and potential of BIoT systems across various industries and use cases, showcasing their adaptability and effectiveness in addressing security needs and challenges.…”

Section: Biot: Integration Of Blockchain and Iot Systemsmentioning

confidence: 99%

Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems

Bian,

Zhang,

Chang

2024

Blockchains

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The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing Internet of Things (IoT) systems. These Blockchain-based IoT (BIoT) systems aim to mitigate security risks such as malicious control, data leakage, and dishonesty often found in traditional cloud-based, vendor-specific IoT networks. As we steadily advance into the era of situation-aware IoT, the use of machine learning (ML) techniques has become essential for synthesizing situations based on sensory contexts. However, the challenge to integrate learning-based situation awareness with BIoT systems restricts the full potential of such integration. This is primarily due to the conflicts between the deterministic nature of smart contracts and the non-deterministic nature of machine learning, as well as the high costs of conducting machine learning on blockchain. To address the challenge, we propose a framework named Situ-Oracle. With the framework, a computation oracle of the blockchain ecosystem is leveraged to provide situation analysis as a service, based on Recurrent Neural Network (RNN)-based learning models tailored for the Situ model, and specifically designed smart contracts are deployed as intermediary communication channels between the IoT devices and the computation oracle. We used smart homes as a case study to demonstrate the framework design. Subsequently, system-wide evaluations were conducted over a physically constructed BIoT system. The results indicate that the proposed framework achieves better situation analysis accuracy (above 95%) and improves gas consumption as well as network throughput and latency when compared to baseline systems (on-chain learning or off-chain model verification). Overall, the paper presents a promising approach for improving situation analysis for BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation.

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Section: Biot: Integration Of Blockchain and Iot Systemsmentioning

confidence: 99%

Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems

Bian,

Zhang,

Chang

2024

Blockchains

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“…Based on Hasse's theorem, the points on a finite elliptic curve E(p, a, b) are bounded as shown in Equation (7).…”

Section: Proxy Re-encryption With Enhanced Salp Swarm Algorithmmentioning

confidence: 99%

“…𝜆 = 𝑦 − 𝑦 𝑥 − 𝑥 , 𝑃≠𝑄 3𝑥 + 𝑎 2𝑦 , ⁄ 𝑃 = 𝑄 In a point 𝑃 ∈ 𝐸 𝑝, 𝑎, 𝑏 , a scalar point multiplication 𝑘 ∈ 𝑍 is denoted in Equa (6). 𝑘𝑃 = 𝑂, 𝑘 = 0 𝑃 + 𝑘 − 1 𝑃 ⁄ , 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒Based on Hasse's theorem, the points on a finite elliptic curve 𝐸 𝑝, 𝑎, 𝑏 are boun as shown in Equation(7).𝑝 + 1 − 2 𝑝 ≤ 𝐸 𝑝, 𝑎, 𝑏 ≤ 𝑝 + 1 + 2 𝑝In the present scenario, there is no effective algorithm in finding 𝑘 ∈ 𝑍 and hardness of this concern forms the basis of ECC. The secret 𝑘 is hidden in the structu 𝑃 = 𝑘𝐺.…”

mentioning

confidence: 99%

Effective Identification and Authentication of Healthcare IoT Using Fog Computing with Hybrid Cryptographic Algorithm

Corthis,

Ramesh,

García-Torres

et al. 2024

Symmetry

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Currently, Internet of Things (IoT)-based cloud systems face several problems such as privacy leakage, failure in centralized operation, managing IoT devices, and malicious attacks. The data transmission between the cloud and healthcare IoT needs trust and secure transmission of Electronic Health Records (EHRs). IoT-enabled healthcare equipment is seen in hospitals that have been implementing the technology for many years. Nonetheless, medical agencies fail to consider the security risk associated with healthcare IoT devices, which are readily compromised and cause potential threats to authentication and encryption procedures. Existing cloud computing methods like homomorphic encryption and the elliptic curve cryptography are unable to meet the security, identity, authentication, and security needs of healthcare IoT devices. The majority of conventional healthcare IoT algorithms lack secure data transmission. Therefore, fog computing is introduced to overcome the problems of IoT device verification, authentication, and identification for scalable and secure transmission of data. In this research manuscript, fog computing includes a hybrid mathematical model: Elliptic Curve Cryptography (ECC) and Proxy Re-encryption (PR) with Enhanced Salp Swarm Algorithm (ESSA) for IoT device verification, identification, and authentication of EHRs. ESSA is incorporated into the PR algorithm to determine the optimal key size and parameters of the PR algorithm. Specifically, in the ESSA, a Whale Optimization Algorithm (WOA) is integrated with the conventional Salp Swarm Algorithm (SSA) to enhance its global and local search processes. The primary objective of the proposed mathematical model is to further secure data sharing in the real time services. The extensive experimental analysis shows that the proposed model approximately reduced 60 Milliseconds (ms) to 18 milliseconds of processing time and improved 25% to 3% of reliability, compared to the traditional cryptographic algorithms. Additionally, the proposed model obtains a communication cost of 4260 bits with a memory usage of 680 bytes in the context of security analysis.

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“…Li et al [11] introduce a secure blockchain-assisted access control scheme tailored for smart healthcare systems in fog computing, ensuring data security and access control. Lakhan et al [12] bring forward the concept of a federated-learning-based privacy preservation system combined with blockchain for fraud detection in IoT healthcare.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

IoT-Driven Healthcare Monitoring with Explainable Machine Learning Models

Rahman,

Jain

2022

RJCSE

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The need for this work arises from the critical importance of IoT-driven healthcare monitoring. In an era marked by technological advancements, the healthcare sector has not been left untouched. The ability to monitor patients' health remotely through IoT devices has emerged as a promising solution, offering real-time data for healthcare providers. However, amidst this promise, there remain significant challenges. Existing approaches in this domain have limitations. They often lack transparency and interpretability, making it challenging to trust the decisions made by machine learning models. Moreover, their performance metrics often fall short of achieving optimal precision, accuracy, recall, AUC, and speed, which are crucial in healthcare applications where timely and accurate decisions. In response to these challenges, this paper presents a novel approach. The proposed model leverages Convolutional Neural Networks (CNN) and integrates Deep Shap and GridCAM++ techniques to offer a more explainable and interpretable solution for IoT-driven healthcare monitoring. This fusion of methods enhances the model's transparency, allowing healthcare professionals to understand the rationale behind its decisions. The advantages of this approach are multifold. First and foremost, it enhances the precision, accuracy, recall, and AUC by 5.5%, 5.9%, 6.5%, and 8.3%, respectively, when compared to existing methods. These improvements translate to more reliable diagnoses and decisions in healthcare. Additionally, the model achieves a 4.9% boost in speed, ensuring that critical decisions can be made swiftly, reducing the time between data collection and actions. The impact of this work is substantial, it paves the way for more trustworthy and efficient IoT-driven healthcare monitoring systems, addressing the limitations of existing approaches. With improved performance metrics and enhanced explainability, healthcare professionals can make more informed decisions, leading to better patient outcomes. Ultimately, this paper contributes to the advancement of healthcare technology, bringing us closer to a future where IoT-enabled monitoring plays a pivotal role in improving patient care sets.

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A Secure Blockchain-Assisted Access Control Scheme for Smart Healthcare System in Fog Computing

Cited by 19 publications

References 32 publications

Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems

Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems

Effective Identification and Authentication of Healthcare IoT Using Fog Computing with Hybrid Cryptographic Algorithm

IoT-Driven Healthcare Monitoring with Explainable Machine Learning Models

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