Intelligent compression then encryption scheme for resource constrained sustainable and smart healthcare environment

Abstract

With the latest and emerging developments of artificial technologies, ubiquitous computing techniques become necessary in day to day lives, particularly in healthcare. Smart healthcare systems involve electronic health records (EHR) sharing system that considerably shares the medical data and delivers proper aid to the patients and research communities. At the same time, the smart healthcare system is a resource constraint environment where large amount of healthcare data needs to be stored on the cloud server. Therefore, data compression can significantly reduce a large amount of data transmission and storage process, thereby achieving effective memory and resource utilization. Compression then encryption (CTE) is an effective way to achieve effective resource utilization and security in the smart healthcare environment. Encryption transforms the EHR data to an illegible format offering considerable authentication of the digital form of medicinal data. With this motivation, this paper designs a novel compression then encryption model for secure healthcare data storage and access management (CTE-SHDSA) technique in cloud server. The proposed model involves two main stages namely compression and encryption. Initially, neighboring indexing sequence (NIS) is employed for the data compression process. The NIS-BWT technique makes use of the correlation among the neighboring bits for reducing the amount of redundant data transmission. Besides, an enhanced artificial butterfly optimization with signcryption (EABO-SC) technique is applied to proficient encrypt the compressed data. The EABO algorithm is applied to optimally choose the encryption keys of the SC technique. The extensive set of simulations was performed on benchmark medical datasets and the outcomes are inspected under several evaluation parameters. The experimental values highlighted the superior performance of the presented method on the recent state of art methods.