Finegrained privacypreserving framework while ensuring data usability in trajectory databases

Abstract

The privacy of trajectories has aroused a wide concern.In previous works,rarely have the differences between different sensitive locations been discussed,nor the differences between different applications(eg:for advertising and for emergencies).While in fact,some sensitive locations are more important and some applications ought to be granted the access.In this paper,to meet different privacy requirements and data utility requirements,we propose a finegrained privacy-preserving framework which allows the users to specify which locations are visible to some applications and invisible to others at the same time.In addition,since most sensitive locations are relevant to stay points and a significant stay in a sensitive place may last longer than the ordinary places,we also propose an efficient approach to distribute invisible location samples along the nearby popular visit sequences.Experiment results indicate that our framework per-forms efficiently without introducing significant performance penalties.