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Privacy preserving two-party k-means clustering over vertically partitioned dataset

  • Zhenmin Lin
  • , Jerzy W. Jaromczyk

Producción científica: Conference contribution › revisión exhaustiva

3 Citas (Scopus)

Resumen

We propose a secure approximate comparison protocol and develop a practical privacy-preserving two-party k-means clustering algorithm over vertically partitioned dataset. Experiments with to real datasets show that the accuracy of clustering achieved with our privacy preserving protocol is similar to the standard (non-secure) kmeans function in MATLAB.

Idioma originalEnglish
Título de la publicación alojadaProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Páginas187-191
Número de páginas5
DOI
EstadoPublished - 2011
Evento2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 - Beijing, China
Duración: jul 10 2011 → jul 12 2011

Serie de la publicación

NombreProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011

Conference

Conference2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
País/TerritorioChina
CiudadBeijing
Período7/10/11 → 7/12/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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