Big Data Based Smart Blockchain for Information Retrieval in Privacy-Preserving Healthcare System

Ali, Aitizaz; Pasha, Muhammad Fermi; Fang, Ong Huey; Khan, Rahim; Almaiah, Mohammed Amin; Hwaitat, Ahmad K. Al

doi:10.1007/978-3-030-87954-9_13

Cited by 14 publications

(11 citation statements)

References 35 publications

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“…Beyond key size, the optimization of block size in cryptographic algorithms also plays a crucial role. Research by 28 on the impact of different block sizes in AES encryption reveals that smaller block sizes can potentially increase the speed of encryption processes but may reduce security against certain types of attacks. This finding is crucial for applications where speed is as important as security, such as in real-time communication systems.…”

Section: Overview Of Cryptography Algorithms Optimizationmentioning

confidence: 99%

“…Nature-inspired algorithms, exemplified by GA and PSO, draw insights from biological and natural processes. On the other hand, non-nature-inspired algorithms, like TS and SA, are grounded in mathematical and physical principles 28 . Additionally, metaheuristics can be differentiated by their search strategies, where some algorithms exploit historical information to guide their search, while others rely on probabilistic decision rules 29 .…”

Section: Literature Reviewmentioning

confidence: 99%

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Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

Hwaitat,

fakhouri,

Tahoun

et al. 2024

Preprint

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In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the Cognitive DL Retinal Blood Vessel Segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net Architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a Matched Filter (MF) for feature enhancement and a post-processing phase employing Morphological Techniques (MT) for refining the segmentation output. Also, the model incorporates Multi-Scale Line Detection and Scale Space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, Retinal Blood Vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

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Section: Overview Of Cryptography Algorithms Optimizationmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

Hwaitat,

fakhouri,

Tahoun

et al. 2024

Preprint

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“…Target nodes and anchor nodes are also included in the neighbor nodes. The target node moves to the location of a nearby node, let us say node 4, and we can assume that since it is equallyclose to the anchor nodes, it is outside the triangulation area [15][16][17].…”

Section: Principle Of Apit Algorithmmentioning

confidence: 99%

A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm

Latha,

Rekha,

Safinaz

2023

RAiSE-2023

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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%

“…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

Self Cite

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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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Big Data Based Smart Blockchain for Information Retrieval in Privacy-Preserving Healthcare System

Cited by 14 publications

References 35 publications

Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

Cryptographic Parameters Enhancement Using Pulsar Orbital Optimization

A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm

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

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