计算机应用研究2023，Vol.40Issue(12)：3778-3783,6.DOI:10.19734/j.issn.1001-3695.2023.04.0141
基于乌鸦搜索的隐私保护聚类算法
Privacy preserving clustering algorithm based on crow search
摘要
Abstract
K-means clustering for differential privacy has the problem of poor data utility.This paper proposed a privacy pre-serving clustering algorithm(CS-PCA)based on crow search and silhouette coefficient.On the one hand,the algorithm used silhouette coefficient to evaluate the clustering effect of each cluster in each iteration,added different amounts of noise accor-ding to the clustering effect,and used the idea of clustering merging to reduce the influence of noise on clustering.On the other hand,it used crow search to optimize the selection of initial centroid in the K-means privacy protection clustering algorithm of differential privacy,and prevented the algorithm from falling into local optimum.The experimental results show the CS-PCA algorithm is more effective for clustering,and also is suitable for large-scale data.As a whole,as privacy budgets continue to grow,the F-measure values of CS-PCA algorithm are 0 to 281.3312％and 4.5876％to 470.3704％higher than DP-KCCM and PADC algorithm respectively.With the same privacy budget,CS-PCA algorithm outperforms the comparison algorithm in terms of availability of clustering results.关键词
乌鸦搜索/轮廓系数/K-means聚类/差分隐私/最优初始质心Key words
crow search/contour coefficient/K-means clustering/differential privacy/optimal initial centroid分类
信息技术与安全科学引用本文复制引用
夏雪薇,张磊,李晶,邓雨康..基于乌鸦搜索的隐私保护聚类算法[J].计算机应用研究,2023,40(12):3778-3783,6.基金项目
黑龙江省自然科学基金联合引导项目(LH2021F054) （LH2021F054）
黑龙江省省属高等学校基本科研业务费优秀创新团队建设项目(2022-KYYWF-0654) （2022-KYYWF-0654）
黑龙江省哲学社会科学研究规划项目(22GLH084) （22GLH084）
佳木斯大学国家基金培育项目(JMSUGPZR2022-014) （JMSUGPZR2022-014）
