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.

The Internet of Things (IoT) connects billions of physical devices around the world to the Internet to collect and share massive data. Location privacy leakage has received considerable attention in the field of security. In order to implement the k -anonymity location privacy protection mechanism, a previous work constructed an anonymous location set by retrieving historical request record database from the trusted third anonymous server, which advantageously protects user’s location privacy. However, the performance of location-based services (LBSs) is weakened by the time overhead of continual retrieving database. Moreover, with the increasingly flourishing of the positioning technique, user location can be accurate to the user’s altitude beyond the two-dimensional latitude and longitude coordinates. In this paper, we present a location privacy protection strategy in LBSs based on Alt-Geohash coding (LPP-AGC) method synthetically considering the altitude of user location and time overhead. Specifically, we give the Alt-Geohash coding algorithm (AGCA) to retrieve the historical request record database, which greatly reduces the time overhead and ensures the immediacy of LBSs. In addition, we propose the dummy location generating algorithm (DLGA) and location filtering algorithm (LFA) to provide users with autonomous k -anonymity location privacy protection. Extensive simulations are performed to verify the performance and security of the proposed strategy.