Open Access

ARTICLE

A Secure Encrypted Classified Electronic Healthcare Data for Public Cloud Environment

Kirupa Shankar Komathi Maathavan^1,*, Santhi Venkatraman²

1 Department of Computer Science and Engineering, Government College of Engineering, Dharmapuri, 636704, Tamilnadu, India
2 Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, 641004, Tamilnadu, India

* Corresponding Author: Kirupa Shankar Komathi Maathavan. Email:

Intelligent Automation & Soft Computing 2022, 32(2), 765-779. https://doi.org/10.32604/iasc.2022.022276

Received 02 August 2021; Accepted 03 September 2021; Issue published 17 November 2021

Abstract

The major operation of the blood bank supply chain is to estimate the demand, perform inventory management and distribute adequate blood for the needs. The proliferation of big data in the blood bank supply chain and data management needs an intelligent, automated system to classify the essential data so that the requests can be handled easily with less human intervention. Big data in the blood bank domain refers to the collection, organization, and analysis of large volumes of data to obtain useful information. For this purpose, in this research work we have employed machine learning techniques to find a better classification model for blood bank data. At the same time, it is vital to manage data storage requirements. The Cloud offers wide benefits for data storage and the simple, efficient technology is adapted in various domains. However, the data to be stored in the cloud should be secured in order to avoid data breaches. For this, a data encryption module has been incorporated into this research work. The combined model provides secure encrypted classified data to be stored in the cloud, which reduces human intervention and analysis time. Machine learning models such as Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbor (KNN) are used for classification. For data security, the Advanced Encryption Standard with Galois/Counter Mode (AES–GCM) encryption model is employed, which provides maximum security with minimum encryption time. Experimental results demonstrate the performance of machine learning and encryption techniques by processing blood bank data.

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Keywords

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