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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vuw5ktdyknehrehttg5qm4rfqm" style="color: black;">2007 IEEE 23rd International Conference on Data Engineering Workshop</a>
With the proliferation of mobile devices (e.g., PDAs, cell phones, etc.), location-based services have become more and more popular in recent years. However, users have to reveal their location information to access location-based services with existing service infrastructures. It is possible that adversaries could collect the location information, which in turn invades user's privacy. There are existing solutions for query processing on spatial networks and mobile user privacy protection in<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdew.2007.4400994">doi:10.1109/icdew.2007.4400994</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icde/KuZPS07.html">dblp:conf/icde/KuZPS07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aanbnn5dyzbhnoqfgncq6gwevu">fatcat:aanbnn5dyzbhnoqfgncq6gwevu</a> </span>
more »... lidean space. However there is no solution for solving queries on spatial networks with privacy protection. Therefore, we aim to provide network distance spatial query solutions which can preserve user privacy by utilizing K-anonymity mechanisms. In this paper, we present two novel query algorithms, PSNN and PSRQ, for answering nearest neighbor queries and range queries on spatial networks without revealing private information of the query initiator. The effectiveness of our privacy protected algorithms has been validated using real world road networks. In addition, we demonstrate the appeal of our technique using extensive simulation results.
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