An Improved Density Peak Clustering Algorithm Based on Chebyshev Inequality and Differential Privacy

This study aims to improve the quality of the clustering results of the density peak clustering (DPC) algorithm and address the privacy protection problem in the clustering analysis process. To achieve this, a DPC algorithm based on Chebyshev inequality and differential privacy (DP-CDPC) is proposed...

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Bibliographic Details
Published in:Applied sciences 2023-08, Vol.13 (15), p.8674
Main Authors: Chen, Hua, Zhou, Yuan, Mei, Kehui, Wang, Nan, Tang, Mengdi, Cai, Guangxing
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Big Data
Chebyshev inequality
Clustering
cosine distance
Data mining
Datasets
dichotomy method
differential privacy
DPC algorithm
Privacy
Privacy, Right of
Semantics
Specific gravity
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Summary:This study aims to improve the quality of the clustering results of the density peak clustering (DPC) algorithm and address the privacy protection problem in the clustering analysis process. To achieve this, a DPC algorithm based on Chebyshev inequality and differential privacy (DP-CDPC) is proposed. Firstly, the distance matrix is calculated using cosine distance instead of Euclidean distance when dealing with high-dimensional datasets, and the truncation distance is automatically calculated using the dichotomy method. Secondly, to solve the difficulty in selecting suitable clustering centers in the DPC algorithm, statistical constraints are constructed from the perspective of the decision graph using Chebyshev inequality, and the selection of clustering centers is achieved by adjusting the constraint parameters. Finally, to address the privacy leakage problem in the cluster analysis, the Laplace mechanism is applied to introduce noise to the local density in the process of cluster analysis, enabling the privacy protection of the algorithm. The experimental results demonstrate that the DP-CDPC algorithm can effectively select the clustering centers, improve the quality of clustering results, and provide good privacy protection performance.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13158674