Implementation and Validation of a Framework for an Ethical and Cyber-secure Smart City

Abstract

In recent years, smart cities have experienced a transformative shift aimed at improving the quality of life for both residents and the broader community. Our research introduces a novel framework that enhances data secrecy and authentication to bolster security in smart city. This paper synthesizes recent advancements in Stochastic Multicriteria Decision Making (SMCDM) and Decision Support Systems (DSS) to address these critical issues. It explores frameworks that model evaluations as random variables, providing a more robust analysis than traditional deterministic approaches. A central focus is the Stochastic Multicriteria Acceptability Analysis (SMAA) family of methods, which operates by exploring the weight space to identify preferences supporting each alternative, thereby circumventing the need for explicit preference elicitation. Key findings highlight SMAA's ability to provide descriptive insights through acceptability indices, central weight vectors, and confidence factors. The synthesis also emphasizes the crucial impact of accounting for dependent uncertainties, demonstrating how their neglect significantly weakens decision support. These methods collectively offer a powerful paradigm for enhancing decision quality in uncertain and group-oriented contexts. The rapid development of smart cities introduces significant ethical and cybersecurity challenges, including data privacy violations, algorithmic bias, and vulnerability to cyberattacks. This paper proposes a novel fuzzy-based multi-criteria decision-making (MCDM) framework for designing, implementing, and validating an ethical and cyber-secure smart city model. The framework integrates fuzzy logic to handle uncertainty in expert evaluations and optimizes the selection of smart city architectures based on ethical compliance and cybersecurity robustness. We evaluate five alternative smart city frameworks using Fuzzy TOPSIS and validate the results through sensitivity analysis, comparative AHP assessment, and real-world case studies.