Drone Helps Privacy: Sky Caching Assisted $k$-Anonymity in Spatial Querying
Hema Naga Sai Sudha Jagarlapudi, Sunho Lim, Jinseok Chae, Gyu Sang Choi, Cong Pu
Abstract
As wireless and mobile devices are prevalent and resource-rich, users can frequently query point-of-interests and enjoy diverse location-based services (LBSs) in infrastructure-based wireless networks. However, users often trade their location privacy with services by sharing their location information with an LBS server, without knowing that the server can fully be trusted. In this article, we propose a sky caching-aided spatial querying scheme with the support of a flying drone. The basic idea is that both the user and its drone collaboratively generate a set of dummy locations to hide the current location of the user from the server. We also propose cache admission control techniques to efficiently cache the queried results to minimize the number of duplicated cached copies. The location privacy is analyzed and measured in terms of the location obfuscation and the size of the convex hull area that consists of dummy locations resided within the cloaking areas of user and drone. We conduct extensive simulation experiments using the OMNeT++ for performance evaluation and comparison. The simulation results show that the proposed approach can reduce the number of queries sent to the server, extend the cloaking area, and improve the location privacy of users significantly.