T‐RBAC Model Based on Two‐Dimensional Dynamic Trust Evaluation under Medical Big Data

Abstract

The professionalism and complexity of medical big data and the expensiveness of acquiring medical knowledge make it difficult for policymakers to judge whether the information accessed by doctors is necessary from a professional perspective and to formulate accurate access control strategies. To solve the above problems, this paper proposes a T-RBAC (trust-role based access control) model based on two-dimensional dynamic trust assessment, Using AHP and Grey theory to quantify the role attribute trust in the dimension of the doctor’s own attributes, Using Euler’s measurement method and probability statistics to quantify doctors’ behavioral trust in the dimension of historical behavior, then, the trust rule base performs hierarchical authorization based on the comprehensive trust value obtained by the weighted average. Multiattribute trust comprehensive evaluation makes the access control model have finer access granularity and higher security. At the same time, the introduction of time decay function and penalty function enhances the model’s sensitivity, dynamics, and resistance to bleaching attacks.

A Blockchain-Based Trustworthy Access Control Scheme for Medical Data Sharing

“…Lewandowski et al [21] improved the treatment effect and smoothed the overall function of the medical system by estimating patient trust. Jiang et al [22] proposed a T-RBAC model utilizing role-attribute trust and physician-behavior trust for hierarchical authorization. Singh and Chatterjee [23] proposed an access control rule set, which can protect unauthorized access to medical data and dynamically control access views.…”

“…This analysis mainly focuses on five aspects such as the dynamics of the trust evaluation model, data encrypted storage, trust privacy, blockchain technology and whether it can resist malicious attacks. The access control scheme designed by Jiang et al [22] and Hu et al [24] sets influence factors to dynamically adjust the accuracy rate of the trust evaluation model, but they cannot guarantee the trust privacy. The HealthyBroker trust-building agent architecture designed by Kurdi et al [19] conducts audit and tracking through the blockchain ledger to prevent potential malicious behavior, but this scheme does not consider the security of patient data.…”