MPDP k-medoids: Multiple partition differential privacy preserving k-medoids clustering for data publishing in the Internet of Medical Things. (October 2021)

• Record Type: Journal Article
• Authors: Zhang, Zekun; Wu, Tongtong; Sun, Xiaoting; Yu, Jiguo
• Is Part Of: International journal of distributed sensor networks. Volume 17:Number 10(2021)
• Publication Date: 2021-10
• Subjects: Data publishing -- differential privacy -- k-medoids clustering -- privacy preservation -- Internet of Medical Things; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501477211042543
• Languages: English
• ISSNs: 1550-1329

Abstract

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.