Evaluation based on multi-criteria decision-making methods and spherical fuzzy framework for security and privacy in metaverse technologies: A case study

Integrating the metaverse technology with the transportation system has several security and privacy issues. This study assesses the 12 security solutions to select the best one to overcome security and privacy issues (such as data theft, unauthorized access, and theft of personal data) when integrating the transportation system with metaverse technology. A suggested methodology is conducted by experts and decision-makers using linguistic terms and spherical fuzzy numbers to express their opinions on evaluating the criteria and alternatives. Selecting the best security solution (alternative) is critical because it includes several conflict security criteria, such as data theft, authentication, security attacks, and others. This paper introduces a methodology for multi-criteria decision-making (MCDM) in a spherical fuzzy (SF) environment. The MCDM method dealt with various conflicting criteria, and SF dealt with uncertainty and vague information while evaluating the criteria and alternatives. The suggested methodology consists of two main phases. The first phase introduces the analytic hierarchy process (SF-AHP) method to compute the criteria weights. The second phase introduces the Weighted Aggregates Sum Product Assessment (SF-WASPAS) method to rank and select the best alternative. The results show the end-to-end authentication protocol is the best alternative (security solution). This study conducted a sensitivity analysis of the stability of the rank by changing the criteria’s weights. The sensitivity analysis results show that the end-to-end authentication protocol is the best alternative (security solution) in different cases. We compare the suggested methodology with six other MCDM methods: SF-TOPSIS, SF-VIKOR, SF-MABAC, SF-CODAS, SF-MARCOS, and SF-COPRAS to show the effectiveness of the proposed method. The results show that the presented methodology is robust compared to other MCDM methods.