Privacy-Preserving Healthcare Analytics of Trajectory Data

Abstract

Technological advancements have led to generation and collection of big data from various data sources including mobile devices. For instance, to prevent, combat and detect COVID-19, citizens of many countries were encouraged to use contact tracing apps on their mobile devices. Collection of their trajectories can be analyzed and mined for social goods. At the same time, their privacy also needs to be preserved. In other words, the advent of COVID-19 has made releasing of patient records become imperative and yet privacy of individuals must be protected. Releasing spatio-temporal COVID-19 data plays a significant role in contact tracing and may help in reducing the spread of the disease due to likelihood of increasing adherence to social distancing and other health related guidelines by the people around the cluster of the released data. In this paper, we examine the problem of preserving privacy of spatio-temporal trajectory data and introduce a hierarchical temporal representative point (HTRP) differential privacy model. We evaluate our framework using a South Korean COVID-19 patient route dataset. Empirical results show a balance of utility and privacy provided by our framework with our HTRP for privacy-preserving healthcare data analytics.