Advertisement
Published in:
2013 | OriginalPaper | Chapter
Density-Based K-Anonymization Scheme for Preserving Users’ Privacy in Location-Based Services
Authors : Hyunjo Lee, Jae-Woo Chang
Published in: Grid and Pervasive Computing
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.