Anonymization models for directional location based service environments

Location based services (LBS) aim to deliver information based on a mobile user's location. However, knowledge of the location can be used by an adversary to physically locate the person, leading to the risk of physical harm, as well as possible leakage of certain personal information. This has serious consequences on privacy. The concept of location k-anonymity has been proposed to address this. Under this notion of anonymity, the adversary only has the knowledge that the LBS request is originating from a region containing at least k people, and therefore cannot individually distinguish the user. However, the existing anonymity models ignore the movement information of mobile users, assuming that it has no impact on privacy. Thus, existing work cannot ensure complete privacy while serving advanced type of LBS requests that require information about direction as well as speed of motion. We denote such LBS services as directional LBS. The key observation we make in this paper is that, in addition to the user's location, the user's movement direction should also be considered to ensure true anonymization. In this paper, we extend the notion of location k-anonymity by incorporating user's moving direction into the anonymization process while serving directional LBS. Specifically, our anonymization methods generalize both location and direction to the extent specified by the user. Our experimental results demonstrate that such anonymization can be achieved with marginal increase in computational cost when compared to the traditional location k-anonymity, while providing increased anonymity.