Secured Health Data Transmission Using Lagrange Interpolation and Artificial Neural Network

Abstract

In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services. The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations. This necessitates the medical field to be redesigned by cloud technology to preserve information about patient's critical diseases, electrocardiogram (ECG) reports, and payment details. The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud. The stored data are categorized as significant and non-significant by Artificial Neural Networks (ANN). The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer. Upon receiving the request from a secondary user, the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user. Once the key matches the key in the hidden layer, the preserved information will be shared between the users. Due to the enhanced privacy preserving key generation, the proposed work prevents the tracking of keys by malicious users. The outcomes reveal that the introduced work provides improved success rate with reduced computational time.