Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking
Open Access
- 3 September 2010
- journal article
- research article
- Published by Oxford University Press (OUP) in American Journal of Epidemiology
- Vol. 172 (9), 1062-1069
- https://doi.org/10.1093/aje/kwq248
Abstract
A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.Keywords
This publication has 16 references indexed in Scilit:
- Musings on privacy issues in health research involving disaggregate geographic data about individualsInternational Journal of Health Geographics, 2009
- Privacy Protection Versus Cluster Detection in Spatial EpidemiologyAmerican Journal of Public Health, 2006
- No Place to Hide — Reverse Identification of Patients from Published MapsThe New England Journal of Medicine, 2006
- A Context-sensitive Approach to Anonymizing Spatial Surveillance Data: Impact on Outbreak DetectionJournal of the American Medical Informatics Association, 2006
- Empirical Bayes methods for disease mappingStatistical Methods in Medical Research, 2005
- A comparison of Bayesian spatial models for disease mappingStatistical Methods in Medical Research, 2005
- Protection of Geoprivacy and Accuracy of Spatial Information: How Effective Are Geographical Masks?Cartographica: The International Journal for Geographic Information and Geovisualization, 2004
- Bayesian approaches to disease mappingPublished by Oxford University Press (OUP) ,2001
- Issues in the statistical analysis of small area health dataStatistics in Medicine, 1999
- Geographically masking health data to preserve confidentialityStatistics in Medicine, 1999