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The emergence and rapid development of the Internet of Medical Things (IoMT),an application of the Internet of Things into the medical and healthcaresystems, have brought many changes and challenges to modern medical andhealthcare systems. Particularly, machine learning technology can be used toprocess the data involved in IoMT for medical analysis and disease diagnosis.However, in this process, the disclosure of personal privacy information mustreceive considerable attentions especially for sensitive medical data. Clusteranalysis is an important technique for medical analysis and disease diagnosis.To enable privacy-preserving cluster analysis in IoMT, this paper proposed anEfficient Differentially Private Data Clustering scheme (EDPDCS) based onMapReduce framework. In EDPDCS, we optimize the allocation of privacy budgetsand the selection of initial centroids to improve the accuracy ofdifferentially private K-means clustering algorithm. Specifically, the numberof iterations of the K-means algorithm is set to a fixed value according to thetotal privacy budget and the minimal privacy budget of each iteration. Inaddition, an improved initial centroids selection method is proposed toincrease the accuracy and efficiency of the clustering algorithm. Finally, weprove that the proposed EDPDCS can improve the accuracy of the differentiallyprivate k-means algorithm by comparing the Normalized Intra-Cluster Variance(NICV) produced by our algorithm on two datasets with two other algorithms.

Achieving Data Utility-Privacy Tradeoff in Internet of Medical Things: A Machine Learning Approach