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However, gathering and exploiting mobility data raises many privacy threats. Sensitive information such as one's home or work place, hobbies, religious beliefs, political or sexual preferences can be inferred from the gathered data. In the last decade, Location Privacy Protection Mechanisms (LPPMs) have been proposed to protect user data privacy. However existing LPPMs fail at effectively protecting the users as most of them reason on local mobility features: micro-mobility (e.g., individual geographical coordinates) while ignoring higher level mobility features, which may allow attackers to discriminate between users. In this paper we propose HMC the first LPPM that reasons on the overall user mobility abstracted using heat maps. We evaluate HMC using four real mobility traces and multiple privacy and utility metrics. The results show that with HMC, across all the datasets 87% of mobile users are successfully protected against re-identification attacks, while others LPPMs only achieve a protection ranging from 43% to 79%. By considering only users protected with a high utility, the proportion of users stays high for HMC with 75%, while for others LPPMs it goes down to proportions between 4% and 43%.<\/jats:p>","DOI":"10.1145\/3264934","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T11:58:41Z","timestamp":1537358321000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["HMC"],"prefix":"10.1145","volume":"2","author":[{"given":"Mohamed","family":"Maouche","sequence":"first","affiliation":[{"name":"Universite de Lyon, CNRS. INSA Lyon, LIRIS, France"}]},{"given":"Sonia","family":"Ben Mokhtar","sequence":"additional","affiliation":[{"name":"Universite de Lyon, CNRS. INSA Lyon, LIRIS, France"}]},{"given":"Sara","family":"Bouchenak","sequence":"additional","affiliation":[{"name":"Universite de Lyon, CNRS. INSA Lyon, LIRIS, France"}]}],"member":"320","published-online":{"date-parts":[[2018,9,18]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2010.05.003"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2011.09.004"},{"key":"e_1_2_1_3_1","volume-title":"Geo-Indistinguishability: Differential Privacy for Location-Based Systems. Ccs'13, abs\/1212.1:--","author":"Andr\u00e9s Miguel E.","year":"2013","unstructured":"Miguel E. Andr\u00e9s , Nicol\u00e1s E. Bordenabe , Konstantinos Chatzikokolakis , and Catuscia Palamidessi . Geo-Indistinguishability: Differential Privacy for Location-Based Systems. Ccs'13, abs\/1212.1:-- , 2013 . Miguel E. Andr\u00e9s, Nicol\u00e1s E. Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. Geo-Indistinguishability: Differential Privacy for Location-Based Systems. Ccs'13, abs\/1212.1:--, 2013."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367531"},{"key":"e_1_2_1_5_1","volume-title":"2nd IEEE Conference on Pervasive Computing and Communications Workshops (PerCom 2004 Workshops)","author":"Alastair","year":"2004","unstructured":"Alastair R. Beresford and Frank Stajano. Mix zones: User privacy in location-aware services . In 2nd IEEE Conference on Pervasive Computing and Communications Workshops (PerCom 2004 Workshops) , 14-17 March 2004 , Orlando, FL, USA, pages 127--131 , 2004. Alastair R. Beresford and Frank Stajano. Mix zones: User privacy in location-aware services. 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