A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS

Almaiah, Mohammed Amin; Hajjej, Fahima; Ali, Aitizaz; Pasha, Muhammad Fermi; Almomani, Omar

doi:10.3390/s22041448

Cited by 88 publications

(56 citation statements)

References 41 publications

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“…STARTGAS: represents the max number of computational steps allowed for the transaction to execute. The data field is the key field that the contracts use to read whenever a smart contract is addressed [ 42 , 43 , 44 , 45 , 46 ]. IIoT Security Threats IIoT solutions consist of industrial systems that connect to cloud for data collection and analysis purposes.…”

Section: Background and Related Studiesmentioning

confidence: 99%

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A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Almaiah

Ali

Hajjej

et al. 2022

Sensors

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The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

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Section: Background and Related Studiesmentioning

confidence: 99%

“…The second level of FN’s latency is reduced because of IoMT device communication. As a result, users’ requirements are encountered in the proposed fog computing (FC) model [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Proposed Frameworkmentioning

confidence: 99%

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Almaiah

Ali

Hajjej

et al. 2022

Sensors

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“…is research [12] examines how changing centralized databases to corner ones might reduce data manipulation. Cloud, fog, blockchain, and IoMT are all parts of the system.…”

Section: Literature Reviewmentioning

confidence: 99%

An Internet of Things (IoT)-Based Optimization to Enhance Security in Healthcare Applications

Shahrani

Rizwan

Chero³

et al. 2022

Mathematical Problems in Engineering

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The Internet of Things (IoT) is a network that connects a large number of items. Each thing uses sensors to create and gather data from its surroundings and then sends to other objects or a central database through a channel. Keeping and transforming this created data is one of the most difficult tasks in IoT today, and it is one of the top worries of all enterprises that deploy IoT technology. Sensing equipment together with communication, storing, and display devices get benefit from technological advancements in the healthcare sector. Moreover, medical parameters and postoperative days require close observation. Therefore, the most cutting-edge approach to healthcare communication is adopted, which makes use of the Internet of Things (IoT). We can use the Internet of Things to speed changes in the healthcare environment, such as enhancing patient involvement and outcomes and shifting healthcare from reactive to proactive accessibility. Nonetheless, the growth of IoT exposes healthcare practitioners and their patients to new vulnerabilities, risks, and security concerns. However, there is currently a scarcity of research on how to improve IoT security in healthcare. Existing studies tend to concentrate only on the installation of IoT peripherals in a healthcare setting and to include a secure application solution. Because healthcare data and information are extremely sensitive, it is critical to have a secure health IoT application in place. As the IoT gets more widely used in healthcare, there will inevitably be more instances of sensitive patient information being made public. This paper proposes an optimized hashing algorithm with digital certificates to enhance the security. Initially, the health data are collected and preprocessed using normalization. The data are then stored in the IoT device. Here, the digital certificates are used for authentication purpose. The proposed discrete decision tree hashing algorithm (DDTHA) with ant colony optimization (ACO) hashes the unsigned digital certificates. The blowfish algorithm is used for encryption, and the signed digital certificate is obtained which is used for authentication purpose. The performance of the proposed system is evaluated and compared with conventional methodologies to prove the efficiency of the system.

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“…The grid providers can formulate a more reasonable and reliable distribution strategy of electricity by the realtime collection and analysis of the situation of electric generation, transmission and demand/consumption [3]. However, the information exchange between the end user and the grid providers may face some security issues and threats [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids

Zhai

Yang

Zhao

et al. 2022

Sensors

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With the development of the Internet of Things, smart grids have become indispensable in our daily life and can provide people with reliable electricity generation, transmission, distribution and control. Therefore, how to design a privacy-preserving data aggregation protocol has been a research hot-spot in smart grid technology. However, these proposed protocols often contain some complex cryptographic operations, which are not suitable for resource-constrained smart meter devices. In this paper, we combine data aggregation and the outsourcing of computations to design two privacy-preserving outsourcing algorithms for the modular exponentiation operations involved in the multi-dimensional data aggregation, which can allow these smart meter devices to delegate complex computation tasks to nearby servers for computing. By utilizing our proposed outsourcing algorithms, the computational overhead of resource-constrained smart meter devices can be greatly reduced in the process of data encryption and aggregation. In addition, the proposed algorithms can protect the input’s privacy of smart meter devices and ensure that the smart meter devices can verify the correctness of results from the server with a very small computational cost. From three aspects, including security, verifiability and efficiency, we give a detailed analysis about our proposed algorithms. Finally, through carrying out some experiments, we prove that our algorithms can improve the efficiency of performing the data encryption and aggregation on the smart meter device side.

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A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS

Cited by 88 publications

References 41 publications

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

An Internet of Things (IoT)-Based Optimization to Enhance Security in Healthcare Applications

Privacy-Preserving Outsourcing Algorithms for Multidimensional Data Encryption in Smart Grids

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