Monitoring personal locations with a probably untrusted server poses privacy threats to the monitored people. To this end, we tend to propose a privacy-preserving location monitoring system for wireless sensor networks. In our system, we tend to design two in-network location anonymization algorithms, particularly, resource and quality-aware algorithms, that aim to enable the system to supply high-quality location monitoring services for system users, while preserving personal location privacy. Both algorithms depend upon the well-established k-anonymity privacy concept, that's, a person is indistinguishable among k persons, to enable trusted sensor nodes to provide the combination location info of monitored persons for our system. Each combination location is during a form of a monitored space A together with the number of monitored persons residing during a, where A contains at least k persons. The resource-aware algorithm aims to minimize communication and computational price, whereas the standard-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing their monitored areas. To utilize the aggregate location information to provide location monitoring services, we use a spatial histogram approach that estimates the distribution of the monitored persons based on the gathered aggregate location data. Then, the estimated distribution is employed to produce location monitoring services through answering vary queries. We have a tendency to evaluate our system through simulated experiments. The results show that our system provides high-quality location monitoring services for system users and guarantees the situation privacy of the monitored persons.
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