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

Zekun Zhang,Tongtong Wu,Jiguo Yu,Xiaoting Sun

doi:10.1177/15501477211042543

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.

Highlights

With the blossom of Internet of Things (IoT)[1,2,3] and 5G,4 intelligent mobile devices and wearable devices are rapidly spreading, and various applications are becoming more and more prevalent, such as social networks, e-commerce, location-based services (LBSs),[5,6,7] and Internet of Medical Things (IoMT) services.[8]
For the data mining process of IMoT data publishing, we propose the MPDP k-medoids method based on differential privacy protection
This article focuses on the privacy protection issues in clustering analysis in IoMT data publishing

Summary

Introduction

With the blossom of Internet of Things (IoT)[1,2,3] and 5G,4 intelligent mobile devices and wearable devices are rapidly spreading, and various applications are becoming more and more prevalent, such as social networks, e-commerce, location-based services (LBSs),[5,6,7] and Internet of Medical Things (IoMT) services.[8]. Combining data clustering with privacy protection techniques in the data publishing process is an effective method to solve the problem of user privacy leakage. This article focuses on data publishing privacy protection in IMoT To achieve both the privacy protection and the availability of user data in data publishing, this article proposes a novel system model for IoMT user data publishing and the multiple partition differential privacy k-medoids (MPDP k-medoids) clustering algorithm for data clustering analysis. To solve the problem of poor availability of data after clustering due to improper selection of the initial center point during the clustering process, the selection of the initial center point is optimized by multiple division of the data set, and a data center point selection algorithm is constructed It combines differential privacy technique in the clustering process, and applies the Laplacian method to add noise data to realize the protection of user privacy data.

Related work

IoMT users

Experimental results

Evaluation index

Declaration of conflicting interests

Conclusion