Article Published In Vol.13 (Jan-Feb 2025)

Hybrid Classification Model for Patient Data Detection using Encryption and Decryption in Cloud Environments

Author : Ruth Athertan

Cloud computing has redesigned the healthcare industry through the means of secure and efficient data management and the timely processing of patient information. However, with the absolute ever-increasing healthcare data, protecting sensitive information during storage and transmission has remained a big challenge. In this paper, a hybrid classification model is proposed: the mechanism integrates well-established encryption techniques, such as RSA for encryption and AES for decryption, with contemporary machine learning methods like CNN (Convolutional Neural Networks) and Autoencoders for data classification. It aims at ensuring the confidentiality and integrity of healthcare data within the cloud space along with a highly accurate and efficient classification of health conditions. The hybrid model hence aims at enhancing the anomalous detection capacity along with thus securing highly cloud-based healthcare systems from data breaches and unauthorized access. Evaluation results have shown that the proposed system is very accurate and reliable, which promotes its potential as a suitable solution for securing healthcare data management.

Keywords: Cloud Computing, Health Care, Healthcare Data Security, Machine Learning, Healthcare Data Management