A Semantic-Based Dummy Generation Strategy for Location Privacy

With the widespread adoption of mobile devices and technical improvement of localization technologies, users can benefit from Location-Based Services (LBSs), which need users' private information. However, users' privacy are vulnerable. Even though the k-anonymity technique has been widely used, adversaries who have external spatiotemporal information still reveal users' privacy. Therefore, in this paper, we propose a novel dummy-location generation mechanism. Firstly, through computing the Super Concept-based Distance (SCD) between candidate dummies and the real location, we select 4k dummies which are semantically similar with the real one. Secondly, by checking the Euclidean distances with the real location, we choose 2k satisfied dummies. Finally, by comparing the multiplication of semantic similarities and Euclidean distance with the real location, we get k–1 dummy locations. Our scheme can generate semantically similar dummy locations, which provides better location protection. Evaluation results show that our method can significantly improve the privacy level in terms of entropy.