Geoprivacy of individual GPS tracking data: A daily activity locations k-anonymity approach for the evaluation of disclosure risk

Keywords: geoprivacy, geomasking, k-anonymity, GPS
Session Type: Virtual Paper Abstract
Day: Sunday
Session Start / End Time: 2/27/2022 11:20 AM (Eastern Time (US & Canada)) - 2/27/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 11

Abstract

Personal privacy is a significant concern in the era of big data. In health geography, personal health data are collected with geographic location information, which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets.