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. Keromytis. Where’s wally?: Precise user discovery attacks in location proximity services. In CCS , 2015. [36] R. A. Popa, A. J. Blumberg, H. Balakrishnan, and F. H. Li. Privacy and accountability for location-based aggregate statistics. In CCS , 2011. [37] A. Pyrgelis, E. De Cristofaro, and G. Ross. Privacy-Friendly Mobility Analytics using Aggregate Location Data. In SIGSPATIAL , 2016. [38] D. Quercia, I. Leontiadis, L. McNamara, C. Mascolo, and J. Crowcroft. Spotme if you can: Randomized responses for location obfuscation on mobile phones. In ICDCS , 2011. [39] V

as a defense to adversarial perturbations against deep neural networks. In IEEE Security and Privacy , 2016. [48] N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Ú. Erlingsson. Scalable Private Learning with PATE. In ICLR , 2018. [49] A. Pyrgelis, C. Troncoso, and E. De Cristofaro. What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy. In PETS , 2017. [50] A. Pyrgelis, C. Troncoso, and E. De Cristofaro. Knock Knock, Who’s There? Membership Inference on Aggregate Location Data. In NDSS , 2018. [51] J. Qian, X.-Y. Li, C

aggregate location data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems , page 34. ACM, 2016. [44] A. E. C. Redondi, D. Sanvito, and M. Cesana. Passive Classification of Wi-Fi Enabled Devices. pages 51–58. ACM Press, 2016. [45] E. Shi, H. T. H. Chan, E. Rieffel, R. Chow, and D. Song. Privacy-preserving aggregation of time-series data. In Annual Network & Distributed System Security Symposium (NDSS) . Internet Society., 2011. [46] S. J. Swamidass and P. Baldi. Mathematical correction for fingerprint