Adaptive Feature Centric Trust Analysis Model for Improved Data Security on EHR Data Using Blockchain

Abstract

Recent development of cloud encourages the healthcare organizations to perform data management and access through variety of services. However, there are number of security issues challenges the QoS of the healthcare systems. To handle the security issues and to enforce data security of Electronic Healthcare Record various approaches are defined in literature. The methods adapt different factors methods like data encryption, key based authentication, and profile based restriction and behavior analysis to restrict the malformed access. However, the methods are inclined to produce poor accuracy in access restriction and QoS maximization. To handle this issue, an Adaptive Feature Centric Trust Analysis Model with Blockchain (AFCTAM-BC) is presented in this article. The method keeps track of feature access performed by any user towards access restriction. By classifying the features of services and healthcare records under different level of sensitivity. Accordingly, the method applies AFCTAM algorithm to measure the trust of any user against the access of different features of healthcare record. The method computes sensitive and non-sensitive Feature Centric Trust Score (FCTS) according to the historic access records. Based on these values, the method computes Access Clearance Measure (ACM) to grant or deny the service access. Further, the method adapts Class Level Data Encryption (CLDE) to generate the blockchain which restrict the malformed access of any user to which the user has no access. The proposed AFCTAM model improves the performance of data security, access restriction and QoS of the healthcare system.