MooD

Abstract

With the increasing development of handheld devices, Location Based Services (LBSs) became very popular in facilitating users' daily life with a broad range of applications (e.g. traffic monitoring, geo-located search, geo-gaming). However, several studies have shown that the collected mobility data may reveal sensitive information about end-users such as their home and workplaces, their gender, political, religious or sexual preferences. To overcome these threats, many Location Privacy Protection Mechanisms (LPPMs) were proposed in the literature. While the existing LPPMs try to protect most of the users in mobility datasets, there is usually a subset of users who are not protected by any of the existing LPPMs. By analogy to medical research, there are orphan diseases, for which the medical community is still looking for a remedy. In this paper, we present MooD, a fine-grained multi-LPPM user-centric solution whose main objective is to find a treatment to mobile users' orphan disease by protecting them from re-identification attacks. Our experiments are conducted on four real world datasets. The results show that MooD outperforms its competitors, and the amount of user mobility data it is able to protect is in the range between 97.5% to 100% on the various datasets. CCS Concepts • Security and privacy → Pseudonymity, anonymity and untraceability.