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The gradual prevalence of Internet of Things (IoT) and wireless communication technologies has enabled the wide adoption of various smart devices (e.g., smart watches) in provisioning the healthcare services to massive users. Besides monitoring the real-time health signals or conditions of users, smart devices can also record a series of sport-related user information such as user location information at a certain time point. The location sequence information is valuable to cluster the users who share the similar sport preferences or habits and therefore, is also playing a key role in providing wireless healthcare services to these users. However, the user location information is often sensitive to certain wireless users as they decline to reveal their daily sport behavior patterns to others. In this situation, a natural challenge is raised in securing the sensitive user location information while mining the users’ daily sport behavior patterns and provisioning better healthcare services to the users. Considering this challenge, we take advantage of the well-known SimHash technique to protect users’ location privacy while clustering the users who share similar sport preferences or habits for better healthcare services. At last, we validate the feasibility of the proposal through a set of simulated experiments conducted on a real-world dataset. Reported results demonstrate that our solution performs better than the other two competitive ones while securing user location information.

Currently, the government proposes various public sports resource allocation strategies to promote the construction of public sports in the local. Thus, searching for the document database becomes an economic and efficient way for government officials to understand the contents of various public sports resource allocation strategies. As the government officials lack background knowledge of public sports resource allocation, the government officials are hard to retrieve appropriate documents. What’s more, the document database is generally unwilling to share all text document information with the government official as some documents contain sensitive data information. Considering the above drawbacks, we put forward a fuzzy keywords-driven public sports resource allocation strategies retrieval approach based on the Simhash (named $PSRAS_{\mathrm {k}+\mathrm {S}}$ ) in the paper. The retrieval approach can accurately recommend appropriate documents to the government officials, by using the text description mining of documents and the fuzzy keywords search technology. Furthermore, the retrieval process is not revealing the sensitive data information of the irrelevant public sports resource allocation strategy document to the government officials, so the retrieval process can achieve local information protection mechanism. Finally, a case study explains the feasibility and effectiveness of our retrieval approach step by step.