5‐2: Unveiling Privacy Challenges: Big Data‐Driven Digital Twins in Smart City Applications

Abstract

Urban centers serve as dynamic hubs of data and information, continually shaping the modern landscape. The fusion of Big Data and Digital Twin (DT) technology plays a pivotal role in advancing smart city initiatives. DT, acting as a comprehensive virtual replica mirroring physical entities' lifecycles, utilizes real‐time data, simulations, and machine learning to enrich decision‐making processes. In urban development, Big Data assumes diverse roles, particularly in urban planning, resource management, and traffic optimization, providing valuable, data‐driven insights to decisionmakers. Simultaneously, DT technology contributes significantly to modeling urban environments, enabling real‐time simulations, and strengthening decision support systems. However, challenges persist, notably in data security and model precision. Addressing these challenges necessitates concerted efforts to enhance data privacy measures and refine the cognitive capabilities of DT models. This paper examines the intricate interplay between Big Data and DT technology in shaping the evolution of smart cities, offering insights into their roles, applications, and implementation challenges. Furthermore, it advocates for future research endeavors aimed at overcoming existing obstacles, thereby fostering secure and effective deployment of Big Data‐driven DT technology and promoting innovative advancements in smart city management and sustainable development.