兵工自动化2023，Vol.42Issue(12)：38-45,8.DOI:10.7690/bgzdh.2023.12.009
基于隐私保护的改进K-means算法
Improved K-means Algorithm Based on Privacy Protection
摘要
Abstract
Aiming at the problem of privacy disclosure in the clustering process of traditional K-means clustering algorithm and the publicity of clustering results,an improved K-means algorithm with differential privacy protection was proposed.On the basis of the original K-means,density measurement is introduced to improve the in-class similarity of clusters and ensure that the selected centers are in relatively dense areas.The distance measure is introduced to reduce the similarity between clusters and ensure the high repulsion of different cluster centers.The average maximum similarity between classes is introduced,and the optimal number of clusters K and the optimal initial intra-class center are dynamically programmed.Privacy protection Laplacian noise is introduced to protect information security.Experimental results show that this algorithm has higher cluster availability and data reliability than traditional algorithms.关键词
差分隐私/K-means聚类/动态规划Key words
differential privacy/K-means clustering/dynamic programming分类
信息技术与安全科学引用本文复制引用
王彩鑫,王丽丽,杨洪勇..基于隐私保护的改进K-means算法[J].兵工自动化,2023,42(12):38-45,8.基金项目
国家自然科学基金(61673200) （61673200）
山东省重大基础研究项目(ZR2018ZC0438) （ZR2018ZC0438）
