Shuffled differential privacy protection method for K-Modes clustering data collection and publication
Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third pa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024004/ |
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| _version_ | 1841540047789096960 |
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| author | Weijin JIANG Yilin CHEN Yuqing HAN Yuting WU Wei ZHOU Haijuan WANG |
| author_facet | Weijin JIANG Yilin CHEN Yuqing HAN Yuting WU Wei ZHOU Haijuan WANG |
| author_sort | Weijin JIANG |
| collection | DOAJ |
| description | Aiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms. |
| format | Article |
| id | doaj-art-707611089d6f4611b4733c061c3ed26a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-707611089d6f4611b4733c061c3ed26a2025-01-14T06:22:42ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-01-014520121359385567Shuffled differential privacy protection method for K-Modes clustering data collection and publicationWeijin JIANGYilin CHENYuqing HANYuting WUWei ZHOUHaijuan WANGAiming at the current problem of insufficient security in clustering data collection and publication, in order to protect user privacy and improve data quality in clustering data, a privacy protection method for K-Modes clustering data collection and publication was proposed without trusted third parties based on the shuffled differential privacy model.K-Modes clustering data collection algorithm was used to sample the user data and add noise, and then the initial order of the sampled data was disturbed by filling in the value domain random arrangement publishing algorithm.The malicious attacker couldn’t identify the target user according to the relationship between the user and the data, and then to reduce the interference of noise as much as possible a new centroid was calculated by cyclic iteration to complete the clustering.Finally, the privacy, feasibility and complexity of the above three methods were analyzed from the theoretical level, and the accuracy and entropy of the three real data sets were compared with the authoritative similar algorithms KM, DPLM and LDPKM in recent years to verify the effectiveness of the proposed model.The experimental results show that the privacy protection and data quality of the proposed method are superior to the current similar algorithms.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024004/shuffled differential privacyK-Modes clusteringprivacy protectiondata collectiondata publication |
| spellingShingle | Weijin JIANG Yilin CHEN Yuqing HAN Yuting WU Wei ZHOU Haijuan WANG Shuffled differential privacy protection method for K-Modes clustering data collection and publication Tongxin xuebao shuffled differential privacy K-Modes clustering privacy protection data collection data publication |
| title | Shuffled differential privacy protection method for K-Modes clustering data collection and publication |
| title_full | Shuffled differential privacy protection method for K-Modes clustering data collection and publication |
| title_fullStr | Shuffled differential privacy protection method for K-Modes clustering data collection and publication |
| title_full_unstemmed | Shuffled differential privacy protection method for K-Modes clustering data collection and publication |
| title_short | Shuffled differential privacy protection method for K-Modes clustering data collection and publication |
| title_sort | shuffled differential privacy protection method for k modes clustering data collection and publication |
| topic | shuffled differential privacy K-Modes clustering privacy protection data collection data publication |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024004/ |
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