Privacy-Preserving Publishing and Visualization of Spatial-Temporal Information
Anifat M. Olawoyin, Carson K. Leung, Alfredo Cuzzocrea
Abstract
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.