Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity

Density peaks clustering is a novel and efficient density-based clustering algorithm. However, the problem of the sensitive information leakage and the associated security risk with the applications of clustering methods is rarely considered. To address the problem, we proposed differential privacy-...

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Bibliographic Details

Published in:	IEEE access Vol. 7; pp. 89427 - 89440
Main Authors:	Sun, Liping, Bao, Shuting, Ci, Shang, Zheng, Xiaoyao, Guo, Liangmin, Luo, Yonglong
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Clustering Clustering algorithms Density density peaks clustering algorithm Differential privacy Euclidean distance Euclidean geometry Heuristic algorithms Privacy Privacy preservation shared near neighbors similarity Similarity
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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