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Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

and Implementation , OSDI’10, Berkeley, CA, USA, 2010. USENIX Association. [31] Kassem Fawaz, Huan Feng, and Kang G. Shin. Anatomization and protection of mobile apps’ location privacy threats. In 24th USENIX Security Symposium (USENIX Security 15) . USENIX Association, 2015. [32] 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 , CCS ’14, 2014. [33] FTC. Android flashlight app developer settles FTC charges it deceived

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Constructing elastic distinguishability metrics for location privacy

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

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Location Privacy with Randomness Consistency

location-based systems. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security, pages 901-914. ACM, 2013. [15] C. A. Ardagna, M. Cremonini, E. Damiani, S. D. C. Di Vimercati, and P. Samarati. Location privacy protection through obfuscation-based techniques. In Data and Applications Security XXI, pages 47-60. Springer, 2007. [16] M. Bellare and P. Rogaway. Random oracles are practical: A paradigm for designing efficient protocols. In Proceedings of the 1st ACM conference on Computer and communications

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Efficient Utility Improvement for Location Privacy

. Chatzikokolakis, and C. Palamidessi, “Optimal geo-indistinguishable mechanisms for location privacy,” in Proc. of CCS , 2014. [8] R. Shokri, “Privacy games: Optimal user-centric data obfuscation,” Proceedings on Privacy Enhancing Technologies , vol. 2015, no. 2, pp. 299–315, 2015. [9] C. Dwork, “Differential privacy,” in Proc. of ICALP , vol. 4052 of LNCS , pp. 1–12, Springer, 2006. [10] “Location guard.” https://github.com/chatziko/location-guard . [11] K. Fawaz and K. G. Shin, “Location privacy protection for smartphone users,” in Proc. of CCS

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Location Privacy for Rank-based Geo-Query Systems

of the 22nd ACM SIGSAC Conference on Computer and Communications Security , pages 1298–1309, 2015. [31] T. Xu and Y. Cai. Feeling-Based Location Privacy Protection for Location-Based Services. In Proceedings of the 16th ACM Conference on Computer and Communications Security , pages 348–357, 2009. [32] Yelp Inc. Yelp API v3. https://www.yelp.com/developers/documentation/v3/business_search , 2017. [Online; accessed 1-March-2017].

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To Permit or Not to Permit, That is the Usability Question: Crowdsourcing Mobile Apps’ Privacy Permission Settings

: Designing for a Party Not a Person. In Proceedings of The New Security Paradigms Workshop (NSPW) . 77–86. DOI: http://dx.doi.org/10.1145/2413296.2413304 [16] Paul D. Ellis. 2009. Thresholds for Interpreting Effect Sizes. (2009). http://www.polyu.edu.hk/mm/effectsizefaqs/thresholds_for_interpreting_effect_sizes2.html . [17] Kassem Fawaz and Kang G Shin. 2014. Location privacy protection for smartphone users. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security . ACM, 239–250. [18] Adrienne Porter Felt, Erika Chin

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