Smart city initiatives increasingly rely on digital twin technology to create comprehensive virtual representations of urban infrastructure. However, the extensive data collection raises significant privacy concerns as aggregated data can reveal sensitive information about individuals. This paper presents a privacy-preserving framework for digital twin technologies in smart city environments that integrates differential privacy mechanisms, federated learning architectures, and privacy-aware data aggregation. We introduce an adaptive privacy budget allocation strategy that dynamically adjusts privacy parameters based on data sensitivity and utility requirements. Experimental evaluation on a realistic smart city digital twin testbed demonstrates that the framework achieves data utility within 4.7% of non-private baselines while providing formal privacy guarantees. The adaptive privacy budget allocation reduces privacy loss by 28% compared to static allocation strategies.
Mohamed et al. (2021) studied this question.