Privacy-preserving digital twin technologies for smart cities: Balancing utility and data protection
2025, vol.17 , no.4, pp. 57-68
Article [2025-04-06]
Digital twins (DTs) have emerged as transformative techn ologies for smart cities, providing real-time virtualization of physical infrastructure and enhancing decision-making capabilities. However, their extensive data collection and integration raise significant privacy concerns. This study introduces a novel privacy-preserving framework for smart city digital twins that balances utility and data protection through multilayered privacy mechanisms. We propose a hybrid architecture integrating differential privacy, federated learning, and secure multi-party computation to protect citizen privacy while maintaining analytical utility. The framework was implemented and evaluated using three real-world datasets: the NYC Open Data transportation feeds, SmartSantander IoT sensor network, and London Air Quality Network. Our comprehensive evaluation measured privacy protection, data utility, computational overhead, and scalability across various urban scenarios. Results demonstrate that our approach achieves 94.7% of the analytical accuracy of non-private systems while providing formal privacy guarantees (ε=2.1). Performance analysis shows the framework can handle real-time data processing with less than 152ms latency for critical smart city applications.
digital twin, smart cities, privacy-preserving technologies, differential privacy, federated learning, secure multi-party computation, data protection, privacy engineering, urban computing
https://doi.org/10.59035/CGYJ7627
Mahmoud Mohamed, Dina Mohamed, Mohamed M H Maatouk. Privacy-preserving digital twin technologies for smart cities: Balancing utility and data protection. International Journal on Information Technologies and Security, vol.17 , no.4, 2025, pp. 57-68. https://doi.org/10.59035/CGYJ7627