Location Privacy Protection via Delocalization in 5G Mobile Edge Computing Environment

In the past several years, we have witnessed a variety of mechanisms for protecting mobile users' location privacy, e.g., k-anonymity, cloaking, encryption, etc. Unfortunately, existing techniques suffer from a common limitation - mobile users' locations must be sent to remote cloud servers. In this article, a novel architecture named LBS@E is proposed for building delocalized location-based services (LBSs) in the 5G mobile edge computing (MEC) environment that do not require users' locations. Mobile users can retrieve local information from LBSs deployed on nearby edge servers based on LBS@E. In this way, LBS@E tackles the location privacy problem innovatively by resolving the root cause of the conventional location privacy problem. However, LBS@E raises new challenges to location privacy. A mobile user can still be localized to a particular privacy area co-covered by the edge servers accessed by the mobile user. A small privacy area puts the mobile user's location at the risk of being approximated. In the meantime, the size of the utility area, which determines the amount of local information retrievable for the mobile user, is positively correlated with the number of edge servers accessed by the mobile user. Thus, given a set of accessible edge servers, the mobile user needs to determine which ones to access so that the retrievable local information is maximized and the risk of being localized is minimized. In this article, we model this new location privacy protecting problem formally, analyze its problem hardness and propose an integer programming based approach for finding the optimal solution. Extensive experiments are conducted on a widely-used real-world dataset to evaluate the proposed approach.