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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Summary: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-preserving density peaks' clustering based on the shared near neighbors similarity method in this paper. First, the Euclidean distance and the shared near neighbors similarity were combined to define the local density of a sample, and the Laplace noise was added to the local density and the shortest distance to protect privacy. Second, the process of cluster center selection was optimized to select the initial cluster centers based on the neighborhood information. Finally, each sample was assigned to the cluster as its nearest neighbor with higher local density. The experimental results on both the UCI and synthetic datasets show that compared with other algorithms, our method more effectively protects the data privacy and improves the quality of the clustering results.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2927308