A Hybrid Trust Evaluation Framework for Cloud Service Providers in Cybersecurity-Critical Environments

Abstract

The rapid expansion of cloud computing has transformed digital service delivery, but establishing the trustworthiness of Cloud Service Providers (CSPs) remains a significant challenge, especially for sensitive workloads. This study presents a novel hybrid evaluation framework aimed at quantifying CSP trustworthiness from multiple perspectives. The framework combines a deterministic Multi-Attribute Trust Model (MATM) with a sophisticated Fuzzy Inference System (FIS). The MATM assesses CSPs based on three key attributes: Cost Efficiency (CE), Performance (P), and Reputation & Trustworthiness (RT). It utilizes a weighted normalization function to compute a transparent Trustworthiness Level (TL), offering a clear quantitative benchmark. To handle the inherent uncertainty and subjectivity in trust evaluation, the framework incorporates an FIS that uses fuzzy logic to interpret linguistic variables and capture the complex relationships among attributes. This dual approach provides both a straightforward, scalable assessment method and a more nuanced, human-centric evaluation. The framework’s effectiveness was validated through scenario analysis. As an initial proof of concept, the evaluation utilized constructed provider scenarios representing typical market profiles. The results demonstrate the model’s ability to clearly differentiate between service offerings, confirming its utility as a comprehensive decision-support tool.