Privacy-Preserving Publishing and Visualization of Spatial-Temporal Information
Anifat M. Olawoyin, Carson Kai-Sang Leung, Alfredo Cuzzocrea · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Partially due to technological advancements as well as the availability of affordable global positioning system (GPS) and cellular devices, more spatio-temporal data can be generated and collected. The presence of spatial and temporal dimensions uniquely differentiate spatio-temporal data from classical data as spatio-temporal data points are structurally related in the context of space and time. In this paper, we present a solution for privacy-preserving publishing and visualization of spatiotemporal big data information. Specifically, it consists of a spatiotemporal hierarchy model (STHM) for some common big data management tasks such as visualization. Our data visualizer provides actionable insight to enhance data-driven decision making. It also enables the discovery of hidden patterns, clusters of events, and outliers. We design two different metrics to preprocess the spatio-temporal for data visualization. Although we demonstrate the usefulness of our solution in privacy-preserving publishing and visualization of spatio-temporal information by using big real-life parking data from two cities, our solution can be applicable for publishing and visualizing spatio-temporal information from many other big data.