Cargando…
A linear programming model for preserving privacy when disclosing patient spatial information for secondary purposes
BACKGROUND: A 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., ensurin...
Autores principales: | Jung, Ho-Won, El Emam, Khaled |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086444/ https://www.ncbi.nlm.nih.gov/pubmed/24885457 http://dx.doi.org/10.1186/1476-072X-13-16 |
Ejemplares similares
-
Physician privacy concerns when disclosing patient data for public health purposes during a pandemic influenza outbreak
por: El Emam, Khaled, et al.
Publicado: (2011) -
Optimization of the Mainzelliste software for fast privacy-preserving record linkage
por: Rohde, Florens, et al.
Publicado: (2021) -
Privacy-preserving storage of sequenced genomic data
por: Hekel, Rastislav, et al.
Publicado: (2021) -
Locational privacy-preserving distance computations with intersecting sets of randomly labeled grid points
por: Schnell, Rainer, et al.
Publicado: (2021) -
A secure protocol for protecting the identity of providers when disclosing data for disease surveillance
por: El Emam, Khaled, et al.
Publicado: (2011)