Confidentiality risks in fine scale aggregations of health data

Spatial confidentiality concerns limit the sharing of data between health data guardians and other researchers. This reduces the contribution GIScience might play in understanding spatial patterns of poor health. This paper takes a first step towards easing data sharing by investigating the confidentiality risks in releasing aggregated data at a fine spatial resolution. A randomly generated cancer map is exported as a graduated color overlay to Google Earth and test subjects are asked to locate where they believe the disease cases reside. Risk is measured by both the separating distance and the number of alternate parcels between the “choice” and a randomly generated disease case. The paper also develops a simulation approach that can be used to test the level of risk involved with these choices. Results suggest that across the scales of aggregation tested in this paper, the finest of which is a 0.5 km grid, there was relatively little risk in revealing sensitive information. In addition, the closest student choice to a disease case was not consistent across aggregations, suggesting no underlying geographic vulnerability. Although the results presented here are encouraging, a series of subsequent investigations are needed before data sharing guidelines can be proposed.

Research highlights
► There is relatively little confidentiality risk in releasing spatial information in mapped aggregations as fine as 0.5 km.
► Potential spatial vulnerabilities were not consistent across aggregations.
► Spatial confidentiality “risk” can be assessed using comparisons to a simulated distribution.