Big Data Privacy Protection and Security Provisions of the Healthcare SecPri-BGMPOP Method in a Cloud Environment

Moorthi Kuttiyappan, Jothi Prabha Appadurai, Balasubramanian Prabhu Kavin, Jeeva Selvaraj, Hong-Seng Gan and Wen-Cheng Lai ()
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Moorthi Kuttiyappan: Department of Computer Science and Engineering, Dr.N.G.P. Institute of Technology, Coimbatore 641048, Tamil Nadu, India
Jothi Prabha Appadurai: Department of CSE (Networks), Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, India
Balasubramanian Prabhu Kavin: Department of Data Science and Business Systems, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Chengalpattu District, Kattankulathur 603203, Tamil Nadu, India
Jeeva Selvaraj: Department of Information Science and Engineering, Jain Deemed to Be University, Global Campus, Bangalore 560069, Karnataka, India
Hong-Seng Gan: School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou 215400, China
Wen-Cheng Lai: Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan

Mathematics, 2024, vol. 12, issue 13, 1-20

Abstract: One of the industries with the fastest rate of growth is healthcare, and this industry’s enormous amount of 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. However, maintaining privacy and security in an untrusted green cloud environment is difficult, so the data owner should have complete data control. A new work, SecPri-BGMPOP (Security and Privacy of BoostGraph 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. The Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, was first applied to the input dataset to begin the clustering process. Second, it was enlarged by employing a piece of the magnifying bit string to generate a safe key; pinpointing-based encryption avoids amplifying 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 Optimisation (HFHBLO). Our proposed method is currently kept in a cloud environment, allowing analytics users to utilise 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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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