Density-based k-anonymization scheme for preserving users' privacy in location-based services

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.