Outlier-eliminated k-means clustering algorithm based on differential privacy preservation

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• 作者：Qingying Yu ; Yonglong Luo ; Chuanming Chen ; Xintao Ding
• 关键词：Differential privacy (DP) preservation ; k ; means clustering ; Outlier ; OEDP
• 刊名：Applied Intelligence
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：45
• 期：4
• 页码：1179-1191
• 全文大小：1,251 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497
• 卷排序：45

文摘

Individual privacy may be compromised during the process of mining for valuable information, and the potential for data mining is hindered by the need to preserve privacy. It is well known that k-means clustering algorithms based on differential privacy require preserving privacy while maintaining the availability of clustering. However, it is difficult to balance both aspects in traditional algorithms. In this paper, an outlier-eliminated differential privacy (OEDP) k-means algorithm is proposed that both preserves privacy and improves clustering efficiency. The proposed approach selects the initial centre points in accordance with the distribution density of data points, and adds Laplacian noise to the original data for privacy preservation. Both a theoretical analysis and comparative experiments were conducted. The theoretical analysis shows that the proposed algorithm satisfies ε-differential privacy. Furthermore, the experimental results show that, compared to other methods, the proposed algorithm effectively preserves data privacy and improves the clustering results in terms of accuracy, stability, and availability.