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In our system, we design two in network location anonymization algorithms, namely, resource- and quality-aware algorithms that aim to enable the system to provide high quality location monitoring services for system users, while preserving personal location privacy. The resource-aware algorithm aims to minimize communication and computational cost, while the quality-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing their monitored areas. Then the estimated distribution is used to provide location monitoring services through answering range queries. To this end, we propose a privacy-preserving location monitoring system for wireless sensor networks. We evaluate our system through simulated experiments. The results show that our system provides high quality location monitoring services for system users and guarantees the location privacy of the monitored persons. Both algorithms rely on the well established k-anonymity privacy concept, that is, a person is indistinguishable among k persons, to enable trusted sensor nodes to provide the aggregate location information of monitored persons for our system.

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In our system, we design two in network location anonymization algorithms, namely, resource- and quality-aware algorithms that aim to enable the system to provide high quality location monitoring services for system users, while preserving personal location privacy. We evaluate our system through simulated experiments. Both algorithms rely on the well established k-anonymity privacy concept, that is, a person is indistinguishable among k persons, to enable trusted sensor nodes to provide the aggregate location information of monitored persons for our system. Then the estimated distribution is used to provide location monitoring services through answering range queries. The results show that our system provides high quality location monitoring services for system users and guarantees the location privacy of the monitored persons. Each aggregate location is in a form of a monitored area A along with the number of monitored persons residing in A, where A contains at least k persons. Monitoring personal locations with a potentially untrusted server poses privacy threats to the monitored individuals.

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Each aggregate location is in a form of a monitored area A along with the number of monitored persons residing in A, where A contains at least k persons. Both algorithms rely on the well established k-anonymity privacy concept, that is, a person is indistinguishable among k persons, to enable trusted sensor nodes to provide the aggregate location information of monitored persons for our system. Then the estimated distribution is used to provide location monitoring services through answering range queries. We evaluate our system through simulated experiments. 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 information. The results show that our system provides high quality location monitoring services for system users and guarantees the location privacy of the monitored persons. In our system, we design two in network location anonymization algorithms, namely, resource- and quality-aware algorithms that aim to enable the system to provide high quality location monitoring services for system users, while preserving personal location privacy. The resource-aware algorithm aims to minimize communication and computational cost, while the quality-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing their monitored areas.

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The resource-aware algorithm aims to minimize communication and computational cost, while the quality-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing their monitored areas. We evaluate our system through simulated experiments. Each aggregate location is in a form of a monitored area A along with the number of monitored persons residing in A, where A contains at least k persons. To this end, we propose a privacy-preserving location monitoring system for wireless sensor networks.

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