Since the advent of hand held devices (e.g., smartphones, tablets, smart watches) with Ubiquitous computing and the wide popularity of location-based mobile applications, the amount of captured user location data is dramatically increasing. However, the gathering and exploitation of this data by mobile application providers raises many privacy threats as sensitive information can be inferred from it (e.g., home and work locations, religious beliefs, sexual orienta-tions and social relationships). To address this issue a number of data obfuscation techniques (also called Location Privacy Protection Mechanisms or LPPMs) have been proposed in the literature. One of the existing methods to assess the effectiveness of LPPMs is to test them against user re-identification attacks. The aim of these attacks is to break user anonymity by re-associating data obfuscated using a given LPPM with user profiles built from user past mobility. In this paper, we present AP-Attack a novel re-identification attack that relies on a heatmap representation of user mobility data. Our experiments run against three representative LPPMs of the literature using four real mobility datasets show that AP-Attack succeeds in re-identifying up to 79% users in non-obfuscated data, +27% more users than POI-Attack and PIT-Attack two well known state-of-the-art attacks. We also present a simple technique to improve user protection against our attack, which relies on a user-centric application of multiple-LPPMs.