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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Detalles Bibliográficos
Autores principales: Yigzaw, Kassaye Yitbarek, Michalas, Antonis, Bellika, Johan Gustav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Technical Advance
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209873/
https://www.ncbi.nlm.nih.gov/pubmed/28049465
http://dx.doi.org/10.1186/s12911-016-0389-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209873/
https://www.ncbi.nlm.nih.gov/pubmed/28049465
http://dx.doi.org/10.1186/s12911-016-0389-x

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