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-...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8756224/ |
| _version_ | 1818855567293153280 |
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| author | Liping Sun Shuting Bao Shang Ci Xiaoyao Zheng Liangmin Guo Yonglong Luo |
| author_facet | Liping Sun Shuting Bao Shang Ci Xiaoyao Zheng Liangmin Guo Yonglong Luo |
| author_sort | Liping Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T08:10:39Z |
| format | Article |
| id | doaj.art-2cd54dfe2ef046a49a52b1b9dc41178a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:10:39Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2cd54dfe2ef046a49a52b1b9dc41178a2022-12-21T20:29:39ZengIEEEIEEE Access2169-35362019-01-017894278944010.1109/ACCESS.2019.29273088756224Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors SimilarityLiping Sun0https://orcid.org/0000-0002-6678-9759Shuting Bao1Shang Ci2Xiaoyao Zheng3Liangmin Guo4Yonglong Luo5School of Computer and Information, Anhui Normal University, Wuhu, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu, ChinaDensity 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.https://ieeexplore.ieee.org/document/8756224/Privacy preservationdifferential privacydensity peaks clustering algorithmshared near neighbors similarity |
| spellingShingle | Liping Sun Shuting Bao Shang Ci Xiaoyao Zheng Liangmin Guo Yonglong Luo Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity IEEE Access Privacy preservation differential privacy density peaks clustering algorithm shared near neighbors similarity |
| title | Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity |
| title_full | Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity |
| title_fullStr | Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity |
| title_full_unstemmed | Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity |
| title_short | Differential Privacy-Preserving Density Peaks Clustering Based on Shared Near Neighbors Similarity |
| title_sort | differential privacy preserving density peaks clustering based on shared near neighbors similarity |
| topic | Privacy preservation differential privacy density peaks clustering algorithm shared near neighbors similarity |
| url | https://ieeexplore.ieee.org/document/8756224/ |
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