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(1):1-9, 1974. [19] B. Gedik and L. Liu. Location privacy in mobile systems: A personalized anonymization model. In Distributed Computing Systems, 2005. ICDCS 2005. Proceedings. 25th IEEE International Conference on, pages 620-629. IEEE, 2005. [20] A. Gendar and A. Lisberg. How cell phone helped cops nail key murder suspect. Secret ’pings’ that gave bouncer away. http://www.nydailynews.com/archives/news/cell-phonehelped-cops-nail-key-murder-suspect-secret-pings-gavebouncer-article-1.599672, 2006. [21] G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.- L. Tan. Private

-Indistinguishable Mechanisms for Location Privacy. In Proceedings of the 21th ACM Conference on Computer and Communications Security , pages 251–262, 2014. [4] K. Chatzikokolakis, M. E. Andrés, N. E. Bordenabe, and C. Palamidessi. Broadening the Scope of Differential Privacy Using Metrics. In Proceedings of the 2013 International Symposium on Privacy Enhancing Technologies , pages 82–102, 2013. [5] R. Dewri. Local Differential Perturbations: Location Privacy Under Approximate Knowledge Attackers. IEEE Transactions on Mobile Computing , 12(12):2360–2372, 2013. [6] R. Dewri, W. Eltarjaman

) Evaluating the privacy risk of location-based services. In: Proceedings of the 15th international conference on Financial Cryptography and Data Security (FC’11), pp 31–46 [11] Gambs S, Killijian MO, Núñez del Prado Cortez M (2014) De-anonymization attack on geolocated data. Journal of Computer and System Sciences 80(8):1597–1614 [12] Gedik B, Liu L (2008) Protecting location privacy with personalized k-anonymity: Architecture and algorithms. IEEE Transactions on Mobile Computing 7(1):1–18 [13] Ghinita G (2013) Privacy for Location-based Services. Morgan & Claypool

1 This work was partially supported by the European Union 7th FP project MEALS, by the project ANR-12-IS02-001 PACE, and by the INRIA Large Scale Initiative CAPPRIS. References [1] https://github.com/paracetamolo/elastic-mechanism . [2] M. E. Andrés, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi. Geo-indistinguishability: differential privacy for location-based systems. In Proc. of CCS , pages 901–914. ACM, 2013. [3] C. A. Ardagna, M. Cremonini, E. Damiani, S. D. C. di Vimercati, and P. Samarati. Location privacy protection through obfuscation

data acquisition for dependable self-driving. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services , pages 371–384. ACM, 2017. [28] Steering angle sensor for automotive applications. http://www.methode.com/Documents/TechnicalLibrary/Steering_Angle_Sensor_Data_Sheet.pdf , 2007. [29] Kassem Fawaz and Kang G Shin. Location privacy protection for smartphone users. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security , pages 239–250. ACM, 2014. [30] Bugra Gedik and Ling Liu

. A classification of location privacy attacks and approaches. Personal and Ubiquitous Computing , 2014. [66] http://wspa.com/2016/01/18/uber-driver-off-the-job-after-he-charged-for-fake-puke-2/ . Last visited: May 2016.

traffic forecasting service. arXiv preprint arXiv:1207.1352 , 2012. [23] J. Kaneps. Apple’s ’differential privacy’ is about collecting your data—but not your data. https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/ , 2016. [24] C. Kopp, M. Mock, and M. May. Privacy-preserving distributed monitoring of visit quantities. In SIGSPATIAL , 2012. [25] J. Krumm. Inference attacks on location tracks. In Pervasive Computing , 2007. [26] J. Krumm. A survey of computational location privacy. Personal and Ubiquitous Computing , 13(6), 2009. [27] S

References [1] J. Krumm, “A survey of computational location privacy,” Personal and Ubiquitous Computing , vol. 13, no. 6, pp. 391–399, 2009. [2] S. Gambs, M.-O. Killijian, and M. N. del Prado Cortez, “Show me how you move and I will tell you who you are,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS , pp. 34–41, ACM, 2010. [3] R. Mendes and J. P. Vilela, “Privacy-Preserving Data Mining: Methods, Metrics, and Applications,” IEEE Access , vol. 5, pp. 10562–10582, 2017. [4] M. Andrés, N. Bordenabe, K

References [1] J. Krumm, “A survey of computational location privacy,” Personal Ubiquitous Comput., vol. 13, no. 6, pp. 391-399, Aug. 2009. [2] ᅳ, “Inference attacks on location tracks,” in Pervasive Computing, vol. 4480, 2007, pp. 127-143. [3] R. Shokri, G. Theodorakopoulos, J.-Y. Le Boudec, and J.- P. Hubaux, “Quantifying location privacy,” in Proc. of the IEEE Symp. on Security and Privacy (S&P), 2011, pp. 247- [4] I. Bilogrevic, K. Huguenin, S. Mihaila, R. Shokri, and J.-P. Hubaux, “Predicting Users’ Motivations behind Location Check-Ins and Utility

. [6] Android. The Android Source Code. https://source.android.com/source/ , 2017. [7] Android. UI/Application Exerciser Monkey. https://developer.android.com/studio/test/monkey.html , 2017. [8] Android. Android Dashboards. https://developer.android.com/about/dashboards/index.html , 2018. [9] C. A. Ardagna, M. Cremonini, S. De Capitani di Vimercati, and P. Samarati. An obfuscation-based approach for protecting location privacy. IEEE Transactions on Dependable and Secure Computing , Jan 2011. [10] Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden