With the increasing development of hand held devices, Location Based Services (LBSs) became very popular 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 work and home places, 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 are always 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 multi-LPPM user centric solution which main objective is to find a treatment to orphan users and protect them from re-identification attacks. Our experiments on the MDC dataset show that the percentage of protected users increased from 64 % to 98% on the considered dataset which is a promising result.