A Data Management System for Smart Cities Leveraging Artificial Intelligence Modeling Techniques to Enhance Privacy and Security

Abstract

Smart cities are metropolitan areas that use sophisticated technology to increase efficiency, sustainability, and overall quality of life. The potential for transformation is tremendous, with applications ranging from Internet of Things (IoT)-driven infrastructure to data-driven governance. Effectively handling the abundant data produced in smart cities requires stringent security and privacy protocols. This research aims to tackle these difficulties by introducing the suggested Artificial Intelligence-based Data Management System (AI-DMS) for Smart Cities. AI-DMS seeks to optimize the data processing pipeline, guaranteeing effectiveness throughout the process, from data extraction to publication. Implementing a Multi-Level Sensitive Model is a notable addition, as it classifies data into three categories: sensitive, quasi-sensitive, and public. This allows for more nuanced sharing of data. Privacy preservation is accomplished using Principal Component Analysis (PCA), a comprehensive technique encompassing feature mapping, selection, normalization, and transformation. The simulation results demonstrate that AI-DMS outperforms other methods. It achieves a Data Quality Score of 95.12% (training) and 93.76% (testing), a Privacy Preservation Rate of 85.23% (training) and 82.76% (testing), a Processing Efficiency of 90.54% (training) and 88.76% (testing), a Sensitivity Model Accuracy of 80.12% (training) and 78.45% (testing), and a Data Access Time of 22.76 ms (training) and 21.32 ms (testing). The results highlight AI-DMS as a reliable and effective system, guaranteeing superior smart city data management that is secure and precise. This contribution aligns with the changing urban scene, offering improvements in decision-making based on data while still ensuring privacy and security.