A Trust Model for Ubiquitous Healthcare Environment on the Basis of Adaptable Fuzzy-Probabilistic Inference System

Abstract

Trust is considered to be a determinant on psychologist selection which can ensure patient satisfaction. Hence, trust concept is essential to be introduced into ubiquitous healthcare (UH) environment oriented on patients with anxiety disorders. This is accomplished by trust model estimating psychologists' trustworthiness, a priory to service delivery, with the use of patient's and his/her acquaintances testimonies, i.e., personal interaction experience and reputation (R). In this paper, a trust model is proposed to be materialized via an adaptable cloud inference system (ACIS) that performs trust value (TV) estimation. Taking advantage of a cloud theory, the introduced ACIS estimates TVs via fuzzy-probabilistic reasoning incorporating a cloud relation operator (soft AND) which is proposed to be tuned by trust information sources consistency and coherency. Theoretical analysis along with comparative study conducted within MATLAB environment and experimental investigation verify the effectiveness of the proposed ACIS materialization under different conditions. Especially, the innovative features of ACIS enable TV to be estimated with 45.5% and 62% on average higher accuracy to that providing state-of-the-art trust models, within clean environment and under the influence of large-scale collusive malicious attacks, respectively. The enhanced robustness permits the untrustworthy UH providers to be discriminated with true positive rate at the range of 0.9 although 40% of R testimonies are erroneous. Finally, experimental investigation validates that the adoption of the proposed trust model for psychologists trustworthiness estimation facilitates patient satisfaction to be achieved into UH environment.

DITrust Chain: Towards Blockchain-Based Trust Models for Sustainable Healthcare IoT Systems

“…A review of digital healthcare systems indicates various services, prototypes and a plethora of trust models designed for distributed environments. Some of which are compatible and others are not due to semantic gaps as well as lack of interoperability [16], [18], [47], [48]. To handle heterogeneous platforms and trust domains, Guangyi proposed a UIF (user interoperability framework) with focus on improving interoperability between IoT devices [16].…”

“…Content may change prior to final publication. for Ubiquitous Healthcare (UH) environments, fuzzyprobabilistic reasoning was used in [18], [58]. Trust can be further strengthened via received feedback upon completion of commitment.…”

“…Furthermore, it helps to enforce a well-founded hierarchy of the linked data sources since the heterogeneously interconnected resources represented by IoT paradigm require a common language to boost the interoperability between the resources as well as offer homogeneous outcomes when multiple resources are queried. Finally, this system must secure and maintain confidentiality between different nodes on the IoT network [10], [18]- [25].…”

“…Encouraged by the above contributions and our intuition to prioritise trust in day to day interactions, focus is given to ensuring that IoT systems with improved interoperability can also secure and guarantee confidentiality between nodes on the IoT infrastructure. Consequently, efforts around buffering decentralised, autonomous and trust capabilities of Blockchains as building blocks of efficient IoT infrastructure is on the increase [11], [18]- [20], [26]. Moreover, as the systems interact, safe interoperability must be maintained by enhancing a bottom-up structure to secure every node added to the network.…”

Health Information Systems in the Digital Health Ecosystem—Problems and Solutions for Ethics, Trust and Privacy

“…Similarly to privacy, fuzzy logic is increasingly used in trust calculation [68][69][70]. Examples for using fuzzy logic include decision-making [71,72], e-commerce [73], multi-criteria trust management [74], and building trust in ubiquitous health care [75].…”