A differential privacy protecting K-means clustering algorithm based on contour coefficients

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor ava...

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Detalles Bibliográficos
Autores principales: Zhang, Yaling, Liu, Na, Wang, Shangping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248925/
https://www.ncbi.nlm.nih.gov/pubmed/30462662
http://dx.doi.org/10.1371/journal.pone.0206832

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248925/
https://www.ncbi.nlm.nih.gov/pubmed/30462662
http://dx.doi.org/10.1371/journal.pone.0206832

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