SECURE ENCRYPTED CLASSIFIED ELECTRONIC HEALTHCARE DATA FOR A PUBLIC CLOUD ENVIRONMENT

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 man- agement 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 com- bined 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), Deci- sion Tree
(DT), Random Forest (RF), Gradient Boosting (GB), KNearest
Neigh- bor (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.