A Systematic Review of Data Security and Privacy-Preserving Models for Smart Cities and Edge Computing Environments

Smart cities have expanded rapidly with the introduction of Internet of Things (IoT) devices and edge computing techniques. Such technologies have transformed how cities function through the ability to deliver smart services and analyze data in real time. But that advancement also adds to the demanding issues about privacy and data security. In smart city environments, privacy-preserving is essential to protect sensitive data collected from different channels against leakage, abuse and unauthorized access. This work is a review of data security and privacy-preserving models made for edge computing and smart cities. It examines the advantages and disadvantages of existing machine learning, blockchain, cryptography, hybrid architectures of optimizing resources, scalability and efficiency. The review discusses open challenges, including interoperability, resource constraints and governance issues, as well as future research directions such as quantum-secure security, federated learning and AI-based adaptive models. The concepts aim to help researchers and policymakers establish strong, durable smart city infrastructures.