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Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation
BACKGROUND: Techniques have been developed to compute statistics on distributed datasets without revealing private information except the statistical results. However, duplicate records in a distributed dataset may lead to incorrect statistical results. Therefore, to increase the accuracy of the sta...
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| Publicado en: | BMC Med Inform Decis Mak |
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| Autores principales: | , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
BioMed Central
2017
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5209873/ https://ncbi.nlm.nih.gov/pubmed/28049465 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12911-016-0389-x |
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