Due to the explosive growth of location-detection devices, such as GPS (Global Positioning System), a user' privacy threat is continuously increasing in location-based services (LBSs). However, the user must precisely disclose his/her exact location to the LBS while using such services. So, it is a key challenge to efficiently preserve a user's privacy in LBSs. For this, the existing method employs a 2PASS cloaking framework that not only hides the actual user location but also reduces bandwidth consumption. However, it suffers from privacy attack. Therefore, we, in this paper, propose a density-based k-anonymization scheme using a weighted adjacency graph to preserve a user's privacy. Our k-anonymization scheme can reduce bandwidth usages and efficiently support k-nearest neighbor queries without revealing the private information of the query initiator. We demonstrate from experimental results that our scheme yields much better performance than the existing one. © 2013 Springer-Verlag.
CITATION STYLE
Lee, H., & Chang, J. W. (2013). Density-based k-anonymization scheme for preserving users’ privacy in location-based services. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7861 LNCS, pp. 536–545). https://doi.org/10.1007/978-3-642-38027-3_57
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