Privacy-preserving models for IoT-based smart city infrastructure

Abstract

Integrating the Internet of Things (IoT) into the infrastructure of smart cities has driven progress in urban management by improving applications such as traffic monitoring, public safety, and utility management. Nonetheless, this network of interconnected data raises significant privacy issues, as IoT devices continually gather and transmit sensitive information that may be susceptible to unauthorized access, breaches, and misuse. Safeguarding privacy within these extensive IoT networks is essential for upholding individual rights and fostering public confidence. This study explores privacy-preserving frameworks designed for IoT-enabled smart city settings, analyzing strategies such as data anonymization, differential privacy, federated learning, and encryption protocols. The effectiveness of each model is evaluated in terms of scalability, computational efficiency, and their ability to adapt to emerging threats. The results emphasize the potential benefits of integrating various privacy-preserving methods to uphold functionality while ensuring data security. Suggestions for applying hybrid models that strike a balance between privacy and operational effectiveness are provided, contributing to the establishment of secure, resilient, and privacy-conscious smart cities.