MVBA\_TSC: Majority Voting and Bayesian Average-Based Trustful Service Composition in Cloud and Edge Environments

The rapid growth in the number of devices connected to the Internet has led to a surge in sophisticated, intelligent service-based applications, exploiting new paradigms and technologies such as edge and cloud computing. These applications essentially result from the interaction between various services. However, this interaction requires the collection, integration, and communication of sensitive data, raising significant concerns about data vulnerability. Users are often concerned about the safety and management of their private information, which makes this crucial issue particularly relevant to the design of automatic services. To address this issue, we propose a novel trustful automatic sensitive service composition approach that leverages machine learning, including the Bayesian average, for accurate trust prediction. The proposed Majority Voting and Bayesian Average-based Trustful Service Composition (MVBA_ TSC) heuristic aims to generate a feasible and efficient service composition by dynamically managing a Trust Service Provider Network (TSPN). Our approach focuses on preserving user privacy by exclusively involving trustworthy service providers in the composition process. This heuristic is geared towards selecting the feasible composition by identifying the most reliable path within the trusted network, considering the Quality of Service (QoS) and trust dimensions among service providers. Experimental results confirmed that MVBA_TSC can improve the trustworthiness of sensitive service composition efficiency by 17 % compared to competitive solutions.