2023
DOI: 10.1109/access.2023.3265954
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A Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization Algorithm for Enhancing the Security of Healthcare Data in Cloud System

Abstract: Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary co… Show more

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Cited by 10 publications
(4 citation statements)
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References 32 publications
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“…As well, Irshad et al 39 introduced an approach, a Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization (MOBFLPSO) algorithm, designed to enhance the security of healthcare data in cloud systems. By combining bee foraging learning principles with PSO, their proposed algorithm aimed to address multiple security objectives simultaneously.…”
Section: Simulation Environment Datasetmentioning
confidence: 99%
“…As well, Irshad et al 39 introduced an approach, a Multi-Objective Bee Foraging Learning-Based Particle Swarm Optimization (MOBFLPSO) algorithm, designed to enhance the security of healthcare data in cloud systems. By combining bee foraging learning principles with PSO, their proposed algorithm aimed to address multiple security objectives simultaneously.…”
Section: Simulation Environment Datasetmentioning
confidence: 99%
“…The same concern should be addressed for the study of 18 , 19 , where the outputs were lacking in optimization 18 and ineffectiveness to preserve other sensitive information, such as frequent items 19 . Another approach, namely the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm, was introduced to yield the optimal key for data sanitization and restoration 20 . The study considered multi-objective functions, such as error rate, computational time, complexity, etc., to measure its performances, but the performances should be more promising.…”
Section: Related Workmentioning
confidence: 99%
“…A real-time reverse transcription-polymerase chain reaction (RTPCR) was the most extensively utilized technology for diagnosis (Zhu et al, 2020). X-ray and CT scans are two widely recognized radiological imaging methods (Irshad et al, 2023) because RT-PCR has a low sensitivity (60-70%), symptoms could be recognized using radiological imaging (Alam et al, 2021b). For instant results, automated evaluation of CT scans and chest X-rays through ML or deep learning approaches were adopted (Çalli et al, 2021), these methods assisted in speeding up the analysis process considerably (Alam et al, 2021a).…”
Section: Understanding Model Choices With Grad-cammentioning
confidence: 99%