Journal
APPLIED INTELLIGENCE
Volume 45, Issue 4, Pages 1179-1191Publisher
SPRINGER
DOI: 10.1007/s10489-016-0813-z
Keywords
Differential privacy (DP) preservation; k-means clustering; Outlier; OEDP
Categories
Funding
- National Natural Science Foundation of China [61370050]
- Natural Science Foundation of Anhui Province [1508085QF134]
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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 epsilon-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.
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