e-Healthsec: A Cloud-Based Privacy-Preserving Electronic Health History Framework using NLP with Multi-Layer Encryption

Background: An effective protection of the large volume of health data and Electronic Health History (EHHs) is a significant challenge in cloud. Physicians need patient’s medical records, including multimedia big data analytics to diagnose patient. Online EHH allows patients to efficiently manage their health records. This can be efficiently achieved by using cloud computing to store EHH. Objectives: The research aims to preserve patients' privacy by efficiently protecting their sensitive EHH in cloud storage by developing a multi-layered encryption framework. Methods: EHHs are encrypted using Fully Homomorphic Encryption (FHE) and Fernet encryption method before being stored in the cloud environment. The proposed system in this work is divided into three modules such as EHH storage after NLP; Key management and authentication; and data storage on the Amazon Web Services (AWS) cloud. Findings: EHH Encryption framework: e-Healthsec using Fernet and FHE which provides multi-layer security for sensitive EHHs.The comparative analysis of performance metrics such as bandwidth, memory usage and level of protection through different encryption techniques determines the Quality of Service (QoS). Novelty and applications: A novel EHH Framework - e-Healthsec that leverages the influence of NLP and text mining with EHH Encryption algorithm to protect sensitive health records.Thusenablesthe healthcare practitioners to control access to patient records while processing and analyzing critical data. Keywords: Electronic Health History, Cloud Computing, Fernet, Natural Language Processing (NLP), Fully Homomorphic Encryption (FHE)