Privacy-Preserving Data Collection and Analysis for Smart Cities

Smart cities leverage real-world data to digitally replicate city-related aspects such as disaster prevention, transportation, and pandemics, creating an encompassing virtual environment. The construction of this realistic virtual world necessitates the collection of individual behavioral data through Internet of Things (IoT) environments. However, the challenge lies in ensuring the privacy of individuals during this data collection process. While numerous studies exist on privacy-preserving data mining, most target clean, complete, and independent personal data. This overlooks the reality of real-world personal data, which often contains noise, missing values, and evidence of interpersonal interactions. To build a human-centric smart city, it is crucial to consider these imperfect and interactive data while preserving privacy. In this paper, we propose a novel framework for privacy-preserving data collection and analysis in smart cities. This framework acknowledges the inherent sensing errors and interpersonal interactions, ensuring a more accurate representation of real-world conditions while maintaining stringent privacy safeguards.