MPDP k-medoids: Multiple partition differential privacy preserving k-medoids clustering for data publishing in the Internet of Medical Things

被引:6
作者
Zhang, Zekun [1 ]
Wu, Tongtong [1 ]
Sun, Xiaoting [1 ]
Yu, Jiguo [2 ,3 ]
机构
[1] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China
[2] Qilu Univ Technol, Shandong Acad Sci, Sch Comp Sci & Technol, Jinan 250353, Peoples R China
[3] Shandong Lab Comp Networks, Jinan, Peoples R China
关键词
Data publishing; differential privacy; k-medoids clustering; privacy preservation; Internet of Medical Things; DATA RELEASE; ALGORITHM; MODEL;
D O I
10.1177/15501477211042543
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The tremendous growth of Internet of Medical Things has led to a surge in medical user data, and medical data publishing can provide users with numerous services. However, neglectfully publishing the data may lead to severe leakage of user's privacy. In this article, we investigate the problem of data publishing in Internet of Medical Things with privacy preservation. We present a novel system model for Internet of Medical Things user data publishing which adopts the proposed multiple partition differential privacy k-medoids clustering algorithm for data clustering analysis to ensure the security of user data. Particularly, we propose a multiple partition differential privacy k-medoids clustering algorithm based on differential privacy in data publishing. Based on the traditional k-medoids clustering, multiple partition differential privacy k-medoids clustering algorithm optimizes the randomness of selecting initial center points and adds Laplace noise to the clustering process to improve data availability while protecting user's privacy information. Comprehensive analysis and simulations demonstrate that our method can not only meet the requirements of differential privacy but also retain the better availability of data clustering.
引用
收藏
页数:15
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