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- W4315705624 abstract "With the rapid development of the information era, data mining is widely used in various fields as a technique that can discover the interrelationship between data, but it also inevitably brings some privacy leakage problems. Differential privacy as an emerging privacy protection technology, because of its strict provability it has received great attention and been widely studied. By analyzing the privacy leakage problem in the traditional K-means algorithm, this paper proposes an improved differential privacy K-means algorithm to improve and protect for the selection of data clustering centers. When improvement, the degree of privacy protection and the availability of the clustering algorithm are analyzed. The initial centroids are selected by using the within-cluster sum of squared errors, and the different privacy intensity assigned to each cluster is calculated by using the silhouette coefficient. Finally, the experimental results demonstrate the feasibility of the differential privacy protection algorithm for data clustering center proposed in this paper." @default.
- W4315705624 created "2023-01-12" @default.
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- W4315705624 date "2023-01-01" @default.
- W4315705624 modified "2023-10-02" @default.
- W4315705624 title "Differential Privacy Protection Algorithm for Data Clustering Center" @default.
- W4315705624 cites W2010523825 @default.
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- W4315705624 doi "https://doi.org/10.1007/978-3-031-20096-0_41" @default.
- W4315705624 hasPublicationYear "2023" @default.
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