Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures

Gordan, Meisam; Siow, Pei Yi; Deifalla, Ahmed Farouk; Ong, Zhi Chao; Ismail, Zubaidah; Khoo, Shin Yee

doi:10.1109/access.2022.3197776

Cited by 6 publications

(3 citation statements)

References 103 publications

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“…It effectively tackles challenges such as security, interoperability, and block storage [52], enabling cryptographic record storage [53]. As a revolutionary and decentralized technology, blockchain safeguards data against unauthorized access [34]. Likewise, this technology is suitable to protect and improve the security of the EHR [28], [40], [43], [45], [46] as it ensures the security, confidentiality, scalability, and integrity of electronic health data, thanks to its distinctive characteristics of decentralization, anonymity, integrity, and tamper-proof verification.…”

Section: Rq3: What Are the Technologies Used To Strengthen Computer S...mentioning

confidence: 99%

A critical review of the state of computer security in the health sector

Jáuregui-Velarde,

Hernández Celis,

Yactayo Arias

et al. 2023

Bulletin EEI

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There is growing concern about IT security in the healthcare sector due to the number of cyberattacks. The objective of the review is to analyze the state of adoption of computer security in the healthcare sector and provide valuable knowledge to researchers and health organizations interested in this field of study. An exhaustive search of international and regional articles on computer security in healthcare organizations was conducted using Scopus, Dimensions, and pubMed databases. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement was used for the selection of articles published between 2018 and 2022. The final number of articles considered is 50. The review explored approaches related to computer security types, mechanisms, and technologies. The findings reveal that blockchain is the most widely used technology to protect medical information. In addition, network, software, and hardware security approaches are employed, using mechanisms such as data encryption, authentication, and access control. Based on these findings, a perimeter security model for the protection of medical information is proposed. In conclusion, these results highlight the importance of adopting robust security measures in terms of networks, software, and hardware, as well as adopting blockchain technology to improve data security in the healthcare sector.

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Section: Rq3: What Are the Technologies Used To Strengthen Computer S...mentioning

confidence: 99%

A critical review of the state of computer security in the health sector

Jáuregui-Velarde,

Hernández Celis,

Yactayo Arias

et al. 2023

Bulletin EEI

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“…Blockchain technology emerged as a transformative solution for secure, transparent, and decentralized transactions [27,28,29]. This is done via use of Multichain-Based Data Sharing Framework with Hierarchical Access Control (MDSFHAC) operations.…”

Section: Literature Reviewmentioning

confidence: 99%

IBMESR: Towards Next-Generation Big Data Security with An Integrated Blockchain Model for Efficient, Scalable, and Robust Operations

Tota,

Karmore

2023

Preprint

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As the utilization of big data environments becomes increasingly widespread, the demand for robust and efficient security mechanisms within this context becomes paramount, especially in real-time scenarios. Ensuring data integrity, trust, and availability in multi-cloud environments is of utmost importance. Existing solutions often fall short in these aspects, resulting in issues such as latency, energy inefficiency, and vulnerability to access attacks, among other drawbacks. Traditional security models designed for big data environments tend to rely on ineffective proof mechanisms and static encryption methods. These conventional approaches lead to increased data processing delays, heightened energy consumption, and reduced communication throughput. Furthermore, these existing models have demonstrated limited capabilities in accurately identifying unauthorized access attempts, thereby escalating security risks. This paper introduces an innovative security framework tailored for big data environments, which leverages a suite of cutting-edge technologies to address the limitations of current models. The proposed architecture integrates several key components, including a Proof of Spatial & Temporal Trust-based Blockchain for secure storage operations, Grey Wolf Optimization (GWO) for efficient blockchain sharding, Fully Homomorphic Encryption (FHE) for safeguarding data across various data nodes, Physical Unclonable Functions (PuFs) for resilient inter-data node communications, and Fuzzy Rule-Based Access Control to enhance overall security. Our research results demonstrate that this proposed model significantly mitigates the inefficiencies and vulnerabilities that currently plague big data security systems. Specifically, it achieves an 8.3% reduction in data processing delays, a 4.5% reduction in energy consumption, a 2.9% improvement in communication throughput, an 8.5% enhancement in the accuracy of detecting access attacks, and a 3.5% increase in access speed levels. This innovative security framework tailored for big data environments offers a comprehensive and effective approach to overcome the limitations of existing solutions. It represents a significant advancement in the field of big data security, contributing significantly to ongoing efforts aimed at creating more secure, efficient, and resilient big data ecosystems.

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“…Advanced, large, and expensive engineering assets such as high-rise buildings, long-span bridges, dams, oil platforms, hydraulic structures, wind turbines, ofshore structures, railways, and ports were designed to last long [1][2][3][4][5][6][7]. However, many of them were more than halfway through their intended service life, and some of them have already reached the end of it [8].…”

Section: Introductionmentioning

confidence: 99%

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

Gordan,

Sabbagh-Yazdi,

Ghaedi

et al. 2023

Advances in Civil Engineering

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Over the last decades, the emergence of new technologies has inspired a paradigm shift for the fourth industrial revolution. For example, circular economy, data mining, and artificial intelligence (AI), which are multidisciplinary topics, have recently attracted industrial and academic interests. Sustainable structural health monitoring (SHM) also concerns the continuous structural assessment of civil, mechanical, aerospace, and industrial structures to upgrade conventional SHM systems. A damage detection approach inspired by the principles of data mining with the adoption of circular-economic thinking is proposed in this study. In addition, vibration characteristics of a composite bridge deck structure are employed as inputs of AI algorithms. Likewise, an artificial neural network (ANN) integrated with a genetic algorithm (GA) was also developed for detecting the damage. GA was applied to define the initial weights of the neural network. To aid the aim, a range of damage scenarios was generated and the achieved outcomes confirm the feasibility of the developed method in the fault diagnosis procedure. Several data mining techniques were also employed to compare the performance of the developed model. It is concluded that the ANN integrated with GA presents a relatively fitting capacity in the detection of damage severity.

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Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures

Cited by 6 publications

References 103 publications

A critical review of the state of computer security in the health sector

A critical review of the state of computer security in the health sector

IBMESR: Towards Next-Generation Big Data Security with An Integrated Blockchain Model for Efficient, Scalable, and Robust Operations

A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence

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