Sport Location-Based User Clustering With Privacy-Preservation in Wireless IoT-Driven Healthcare
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, sm...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/0fafd1c62d0a4ad3b318406bbb8d0768 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | 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. |
|---|