ArticleInternational journal of health geographics2014
A linear programming model for preserving privacy when disclosing patient spatial information for secondary purposes.
Article in International journal of health geographics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward.JMIR medical informatics · 2022Article
- Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information.Annals of the American Association of Geographers · 2022Article
- Using mobile location data in biomedical research while preserving privacy.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Anonymisation of geographical distance matrices via Lipschitz embedding.International journal of health geographics · 2016Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA linear programming (LP) model was proposed to create de-identified data sets that maximally include spatial detail (e.g., geocodes such as ZIP or postal codes, census blocks, and locations on maps) while complying with the HIPAA Privacy Rule's Expert Determination method, i.e., ensuring that the risk of re-identification is very small. The LP model determines the transition probability from an original location of a patient to a new randomized location. However, it has a limitation for the cases of areas with a small population (e.g., median of 10 people in a ZIP code).
methodsWe extend the previous LP model to accommodate the cases of a smaller population in some locations, while creating de-identified patient spatial data sets which ensure the risk of re-identification is very small.
resultsOur LP model was applied to a data set of 11,740 postal codes in the City of Ottawa, Canada. On this data set we demonstrated the limitations of the previous LP model, in that it produces improbable results, and showed how our extensions to deal with small areas allows the de-identification of the whole data set.
conclusionsThe LP model described in this study can be used to de-identify geospatial information for areas with small populations with minimal distortion to postal codes. Our LP model can be extended to include other information, such as age and gender.
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