| Mapping health data: improved privacy protection with donut method geomasking. | |
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MedLine Citation:
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PMID: 20817785 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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. |
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Authors:
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Kristen H Hampton; Molly K Fitch; William B Allshouse; Irene A Doherty; Dionne C Gesink; Peter A Leone; Marc L Serre; William C Miller |
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Publication Detail:
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Type: Comparative Study; Journal Article; Research Support, N.I.H., Extramural Date: 2010-09-03 |
Journal Detail:
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Title: American journal of epidemiology Volume: 172 ISSN: 1476-6256 ISO Abbreviation: Am. J. Epidemiol. Publication Date: 2010 Nov |
Date Detail:
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Created Date: 2010-10-25 Completed Date: 2010-11-16 Revised Date: 2011-11-01 |
Medline Journal Info:
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Nlm Unique ID: 7910653 Medline TA: Am J Epidemiol Country: United States |
Other Details:
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Languages: eng Pagination: 1062-9 Citation Subset: IM |
Affiliation:
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Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, 27599-7030, USA. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Cluster Analysis Confidentiality* Disease Outbreaks* Epidemiologic Methods* Geographic Information Systems* Humans North Carolina / epidemiology Population Surveillance |
| Grant Support | |
ID/Acronym/Agency:
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R01 AI067913/AI/NIAID NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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