Version 1
: Received: 4 April 2024 / Approved: 4 April 2024 / Online: 4 April 2024 (14:32:08 CEST)
How to cite:
K, M.; Appadurai, J.P.; Kavin, B.P.; Selvaraj, J.; Gan, H.; Lai, W. Big Data Privacy Protection and Security Provisions of Healthcare SecPri-BGMPOP Method in Cloud Environment. Preprints2024, 2024040386. https://doi.org/10.20944/preprints202404.0386.v1
K, M.; Appadurai, J.P.; Kavin, B.P.; Selvaraj, J.; Gan, H.; Lai, W. Big Data Privacy Protection and Security Provisions of Healthcare SecPri-BGMPOP Method in Cloud Environment. Preprints 2024, 2024040386. https://doi.org/10.20944/preprints202404.0386.v1
K, M.; Appadurai, J.P.; Kavin, B.P.; Selvaraj, J.; Gan, H.; Lai, W. Big Data Privacy Protection and Security Provisions of Healthcare SecPri-BGMPOP Method in Cloud Environment. Preprints2024, 2024040386. https://doi.org/10.20944/preprints202404.0386.v1
APA Style
K, M., Appadurai, J.P., Kavin, B.P., Selvaraj, J., Gan, H., & Lai, W. (2024). Big Data Privacy Protection and Security Provisions of Healthcare SecPri-BGMPOP Method in Cloud Environment. Preprints. https://doi.org/10.20944/preprints202404.0386.v1
Chicago/Turabian Style
K, M., Hong-Seng Gan and Wen-Cheng Lai. 2024 "Big Data Privacy Protection and Security Provisions of Healthcare SecPri-BGMPOP Method in Cloud Environment" Preprints. https://doi.org/10.20944/preprints202404.0386.v1
Abstract
One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.
Keywords
Big Data; Security; Privacy; Boost Graph convolutional network clustering algorithm; Magnify Pinpointing based encryption approach; Hybrid Particle swarm; Grey wolf optimization
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
