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Related Experiment Video

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An Application for Pairing with Wearable Devices to Monitor Personal Health Status

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Geomasking sensitive health data and privacy protection: an evaluation using an E911 database.

William B Allshouse¹, Molly K Fitch, Kristen H Hampton

¹The University of North Carolina at Chapel Hill, Gillings School of Global Public Health, Department of Environmental Sciences and Engineering, Chapel Hill, 27599-7431 United States.

Geocarto International

|October 19, 2010

Summary

Donut geomasking privacy protection may fail in areas with uneven population distribution. Researchers suggest increasing the displacement area to enhance security for heterogeneous populations.

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Last Updated: Jun 8, 2026

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

Spatial data privacy
Geographic Information Systems (GIS)
Data anonymization techniques

Background:

Geomasking methods, including donut geomasking, are crucial for protecting individual address privacy in spatial datasets.
These methods often assume uniform population distribution for accurate privacy protection, which can be a limitation.

Purpose of the Study:

To evaluate the effectiveness of donut geomasking in Orange County, North Carolina, using 2007 household data.
To assess the impact of population distribution heterogeneity on privacy protection levels.

Main Methods:

Calculated estimated k-anonymity for households assuming uniform distribution.
Determined actual k-anonymity by comparing with E911 database household locations.
Analyzed privacy protection in census block groups with varying population densities.

Main Results:

Privacy protection was found to be insufficient in mixed-use areas with heterogeneous population distributions.
Census block groups with high population distribution heterogeneity were most vulnerable to under-protection.
The study identified specific areas where donut geomasking failed to meet privacy criteria.

Conclusions:

The assumption of homogeneous population distribution in geomasking algorithms can lead to inadequate privacy.
For heterogeneous populations, tripling the minimum displacement area in donut geomasking is proposed.
This adjustment aims to maintain privacy with a minimal error rate of less than 1%.