A dummy-based anonymization method based on user trajectory with pauses

Abstract

A variety of services utilizing users' positions have become available because of rapid advances in Global Positioning System (GPS) technologies. Since location information may reveal private information, preserving location privacy has become a significant issue. We proposed a dummy-based method of anonymizing location to protect this privacy in our previous work that generated dummies based on various restrictions in a real environment. However, the previous work assumed a simplified mobility model in which users kept moving and did not stop. If we assume a more realistic mobility model in which users often pause to visit various attractions, it becomes increasingly more difficult to generate dummies that will move naturally. In this paper, we assumed that the users' movements are known in advance and propose a dummy-based anonymization method based on user movements, where dummies move naturally while stopping at several locations. We simulated user movements on real map information and verified the method we propose was more effective than the previous one.

Security and Privacy in Location-Based Services for Vehicular and Mobile Communications: An Overview, Challenges, and Countermeasures

“…The authors in [112], [113] propose dummy-based privacypreserving techniques which are based on assumptions that the movement plan of the user is known in advance. In a more realistic and refined approach [31], they propose a dummybased privacy-preserving technique to anonymize the location of users in real environments.…”

Perturbed Anonymization : Two Level Smart Privacy for LBS Mobile Users

“…Privacy Enhanced Trusted location based services (PE-TLBS) [18] focus to implement a simple protocol in which the user authenticate the server, while preserving anonymity and avoiding the possibility of their personal information leakage. The concept of dummy nodes proposes the use of dummy locations with the real location to protect the location privacy of the node [19]. The quality of requested service degrades when the number of dummy node increases.…”

Users Collaborative Mix-Zone to Resist the Query Content and Time Interval Correlation Attacks

“…This type of fashion makes privacy preservation of user collaboration no longer efficient, because they often submit queries with certain time delays. Thus, researchers have to go back to TTP for finding solutions and solutions of [1,3,4,7,9,10,26,27] are able to be classified into two categories: whole trajectory disturbance and subsequent locations generalization. As the adversary can obtain user's subtrajectory, algorithms of the whole trajectory disturbance are less suitable for continuous LBSs, so algorithms of subsequent locations generalization prosperous.…”

“…These algorithms can be classified into two main categories: the whole trajectory disturbance [1][2][3][4] and the subsequent location generalization [5][6][7][8][9][10]. As subtrajectories can be obtained before preserving, the adversary may infer the location correlation and identify the real trajectory from the anonymous group, which makes algorithms of whole trajectory disturbance failed.…”