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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Vydané v:	IEEE access Ročník 7; s. 89427 - 89440
Hlavní autori:	Sun, Liping, Bao, Shuting, Ci, Shang, Zheng, Xiaoyao, Guo, Liangmin, Luo, Yonglong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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