Metaheuristic Optimization Based Multi-Key Enabled Encryption With Image Classification for Big Data Environment in Healthcare Sector

Abstract

In recent times, medical imaging domain undergoes significant development with respect to innovations, market growth, and exploitation with the rise in the generation of massive data quantity poses diagnostic imaging in the context of Big data. At the same time, securing medical images is needed and it remains a crucial process on the shared communication model. Encryption is considered an effective way of securing the data transmission process in big data environment. Besides, the computer aided diagnosis also provides a second opinion to professionals to manage parallelism. So, it becomes essential to design digital environments and applications which offer effective handling of medical images such as Big data. This paper presents a new Metaheuristic Optimization based Multi-Key Enabled Encryption with Image Classification (MOMEE-IC) for Big Data Environment in Healthcare Sector. The proposed model involves two major operations as encryption and image classification. Firstly, the encryption process involves Multiple key based Homomorphic Encryption (MHE) with lion optimization algorithm (LOA) based optimal key generation process, called MHE-LOA. Besides, Stacked Denoise Autoencoder with Logistic Regression (SDAE-LR) based image classification process is employed for diagnosing the images in the cloud platform. To ensure the superior results of the presented model, a wide set of simulations were performed and the results are examined under distinct aspects.