牡丹江师范学院学报（自然科学版）Issue(2)：6-13,8.
基于k-modes聚类算法的混洗差分隐私方法
Shuffled Differential Privacy Method based on k-modes Clustering Algorithm
摘要
Abstract
This paper proposes for the first time a shuffling differential privacy protection scheme(SDPk-modes)based on k-modes clustering algorithm.SDPk-modes are divided into different groups according to the distance between each data to obtain enough fine-grained optimization effect.The gradient stochastic perturbation technology is used to calculate the optimal probability less time.In the process of k-modes clustering,the feature vector that frequently appears in the data is taken as the cluster center point,and the distance measurement method based on attribute entropy speeds up the algorithm convergence to the cluster center,solves the problems of slow clustering speed and easy to fall into local optimality of the original algorithm,and significantly improves the clustering effect.Experimental verification shows that the proposed scheme is superior to the current similar schemes.关键词
混洗差分隐私/k-modes/随机响应机制/隐私保护Key words
shuffled differential privacy/k-modes/random response mechanism/privacy protection分类
信息技术与安全科学引用本文复制引用
祁富,陈丽敏..基于k-modes聚类算法的混洗差分隐私方法[J].牡丹江师范学院学报（自然科学版）,2024,(2):6-13,8.基金项目
黑龙江省自然科学基金项目(LH2019F051) （LH2019F051）
牡丹江师范学院科技创新重点项目(kjcx2023-126mdjnu) （kjcx2023-126mdjnu）
