Measurement: Sensors (Dec 2022)
CUNA: A privacy preserving medical records storage in cloud environment using deep encryption
Abstract
Securing the medical images in cloud environment becomes mandatory because of massive image accumulation and information storage in cloud premises. Large accommodation of information in the cloud brings another risk of data security. Privacy preserving data mining techniques investigate privacy of data to be maintained before or either after the data mining technique. Lightweight technique to store the patient data securely in the cloud is focused. The proposed study is focused on designing a robust model in which medical records privacy is preserved using Customizable Unique node access (CUNA) architecture. The system design undergoes data preparation, encryption, cloud transfer, decryption and analysis modules. The input to the proposed system is patient health care data such as CT images, Medical records, Clinical treatment procedures etc. The CT images are encrypted with respect to their medical records, further transferred into the cloud by hiding the text based confidential data into the images. Inspite of normal cloud storage process, the proposed approach utilized deep cryptography technique to make authenticated storage mechanism adaptive in the cloud. CUNA is the customized commonly allocated key assigned at every node. The unique key word is encrypted, and utilized to transfer the privacy data of the patients. The data can be retrieved from the cloud, after the decryption of CUNA code. The correlation of the key is systematically achieved using Gaussian regression process algorithm (GRA). The proposed approach is compared with various state-of-art approaches and obtained the accuracy of 92% with image MSE of 0.0012%.
