Implementing Privacy Preserving Techniques in Data Sharing Platforms for Smart Cities

Abstract

Smart cities leverage vast amounts of data to enhance urban services, improve infrastructure, and optimize resource management. However, the collection and sharing of such data raise significant privacy concerns. This paper explores privacy-preserving techniques that can be integrated into data-sharing platforms for smart cities to protect sensitive information while maintaining data utility. We review cryptographic methods, anonymization strategies, and federated learning approaches that balance privacy and data usability. Additionally, we present case studies and comparative analyses of existing solutions. Our findings highlight the importance of adopting hybrid privacy models to address evolving threats in smart city ecosystems.