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...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 17; pp. 1 - 20 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
1/3/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |