Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

Abstract

Background: Personal privacy is a significant concern in the era of big data. In the field of 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 trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method.

Methods: 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.

Results: The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual's home and all the other daily activity locations.

Conclusions: 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. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.

Keywords: Confidential geospatial data; GPS datasets; Geomasking; Geoprivacy; k-anonymity.

Fig. 1

An example of the calculation of spatial k-anonymity. a The blue star represents the person’s original location, and the green dots represent the potential residential locations around the person; b the person’s masked location is calculated by a geomasking technique and represented as a red star, and the masking distance is d; c a buffer with a radius of d is created around the masked location, and the number of potential residential locations (highlighted green dots) in the buffer area, including the person’s original location, is the value of k for spatial k-anonymity

Fig. 2

A heuristic example illustrating the calculation of DAL k-anonymity. a The simulated raw GPS tracking data; b geomasked GPS tracking data; c activity locations detected from the raw GPS tracking data; d activity locations detected from the geomasked GPS tracking data; e the distance between the activity locations detected from the raw GPS tracking data and the geomasked GPS data; f the respective probability of re-identifying the different activity locations

Fig. 3

DAL k-anonymity (blue line) and spatial k-anonymity (orange line) with the changing numbers of potential activity locations around home location from 1 to 50

Fig. 4

DAL k-anonymity (blue line) and spatial k-anonymity (orange line) with the changing time the person spends at home from 6 to 24 h

Fig. 5

DAL k-anonymity (blue line) and spatial k-anonymity (orange line) for various numbers of potential activity locations around other activity locations from 1 to 50

Fig. 6

DAL k-anonymity (blue line) and spatial k-anonymity (orange line) for various ratios of time spent at other activity locations

Fig. 7

DAL k-anonymity (blue line) and spatial k-anonymity (orange line) in various number of other activity locations

Fig. 8

DAL k-anonymity (blue line) and spatial k-anonymity (orange line) for various time the person spends on travel