Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

TLDR

Smart cities generate large amounts of industrial data from transportation, healthcare, business, and social activities, and fusing and mining this data can improve applications, but the data often contain sensitive spatial‑temporal context that raises privacy concerns. The study aims to secure user privacy in smart‑city industrial data before integration for mining, analysis, and prediction, addressing the trade‑off between privacy protection and accurate outcomes. The authors propose a privacy‑aware data fusion and prediction method using locality‑sensitive hashing, evaluated on a real‑world dataset. Experimental results show that the proposed approach achieves better prediction performance than other competitive methods.

Abstract

As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users' context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.