A novel trajectory privacy-preserving future time index structure in moving object databases

Abstract

The next generation of location-based services has been being predicted to achieve its superior development over the coming years. Keeping pace with this growth are new trends of predictive applications emerging to meet the demands of end-users and satisfy their matters of life. The violation of users' private information from their position disclosure, however, cuts off their beliefs when they enjoy such services. In this paper, therefore, we propose a novel index structure known as PPST-tree, which is able to deal with predictive and aggregate queries and is aware of trajectory privacy protection towards future positions of moving objects. Moreover, the prediction model and related strategies are also introduced in order to support location-based applications whereas user privacy is still preserved. Last but not least, privacy analyses and performance experiments show how well the proposed method can help. © 2012 Springer-Verlag.