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Turtles, Locks, and Bathrooms: Understanding Mental Models of Privacy Through Illustration

definitions and perspectives. In CHI’08 Extended Abstracts on Human Factors in Computing Systems , pages 3291–3296. ACM, 2008. [32] C. Lang and H. Barton. Just untag it: Exploring the management of undesirable facebook photos. Comput. Human Behav ., 43:147–155, Feb. 2015. [33] S. Lederer, A. K. Dey, and J. Mankoff. Everyday privacy in ubiquitous computing environments. In Ubicomp Privacy Workshop , 2002. [34] J. Lin, S. Amini, J. I. Hong, N. Sadeh, J. Lindqvist, and J. Zhang. Expectation and purpose: Understanding users’ mental models of mobile app

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Hardware-Supported ORAM in Effect: Practical Oblivious Search and Update on Very Large Dataset

. [8] A. Boldyreva, N. Chenette, Y. Lee, and A. O’neill. Order-preserving symmetric encryption. In Annual International Conference on the Theory and Applications of Cryptographic Techniques , pages 224–241. Springer, 2009. [9] R. Bost, B. Minaud, and O. Ohrimenko. Forward and backward private searchable encryption from constrained cryptographic primitives. Technical report, IACR Cryptology ePrint Archive 2017, 2017. [10] F. Brasser, U. Müller, A. Dmitrienko, K. Kostiainen, S. Capkun, and A. Sadeghi. Software Grand Exposure: SGX Cache Attacks Are

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Private Set Intersection for Unequal Set Sizes with Mobile Applications

– EUROCRYPT’15 , ser. LNCS, vol. 9056. Springer, 2015, pp. 673–701. [4] N. Asokan, A. Dmitrienko, M. Nagy, E. Reshetova, A. Sadeghi, T. Schneider, and S. Stelle, “CrowdShare: Secure mobile resource sharing,” in Applied Cryptography and Network Security (ACNS’13) , ser. LNCS, vol. 7954. Springer, 2013, pp. 432–440. [5] P. Baldi, R. Baronio, E. De Cristofaro, P. Gasti, and G. Tsudik, “Countering GATTACA: efficient and secure testing of fully-sequenced human genomes,” in ACM Computer and Communications Security (CCS’11) . ACM, 2011, pp. 691–702. [6] D

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Privacy-preserving Wi-Fi Analytics

International on Workshop on Physical Analytics , WPA ’16, pages 43–48, New York, NY, USA, 2016. ACM. [3] M. Alaggan, S. Gambs, and A.-M. Kermarrec. BLIP: Non-Interactive Differentially-Private Similarity Computation on Bloom Filters. In Proceedings of the 14th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS’12) , Toronto, Canada, October, 2012. [4] M. Alaggan, S. Gambs, S. Matwin, and M. Tuhin. Sanitization of Call Detail Records via Differentially-Private Bloom Filters. In P. Samarati, editor, Data and Applications

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Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute

] Goodrich, Michael T. 2011. Data-oblivious External-memory Algorithms for the Compaction, Selection, and Sorting of Outsourced Data . In: SPAA . [25] Goodrich, Michael T 2014. Zig-zag sort: A simple deterministic data-oblivious sorting algorithm running in o (n log n) time . In: STOC . [26] Goodrich, Michael T., and Mitzenmacher, Michael 2010. Privacy-preserving access of outsourced data via oblivious RAM simulation. CoRR , abs/1007.1259. [27] Goodrich, Michael T, Ohrimenko, Olga, and Tamassia, Roberto 2012. Data-oblivious graph drawing model and

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New Privacy Threat on 3G, 4G, and Upcoming 5G AKA Protocols

Security Privacy , 13(3):24–31, May 2015. [19] David Basin, Jannik Dreier, Lucca Hirschi, Saša Radomirovic, Ralf Sasse, and Vincent Stettler. A Formal Analysis of 5G Authentication. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security , pages 1383–1396. ACM, 2018. [20] A. N. Bikos and N. Sklavos. LTE/SAE Security Issues on 4G Wireless Networks. IEEE Security Privacy , 11(2):55–62, March 2013. [21] B. Blanchet. An Efficient Cryptographic Protocol Verifier Based on Prolog Rules. In Proceedings of CSFW’01 , pages 82

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Toward Distribution Estimation under Local Differential Privacy with Small Samples

(ICALP’06), pp 1-12 [13] Dwork C, Roth A (2014) The Algorithmic Foundations of Differential Privacy. Now Publishers [14] Eagle N, Pentland A, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences (PNAS) 106(36):15,274-15,278 [15] Efron B, Hastie T (2016) Computer Age Statistical Inference. Cambridge University [16] Eltarjaman W, Dewri R, Thurimella R (2017) Location privacy for rank-based geo-query systems. Proceedings on Privacy

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SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

. Provably private data anonymization: Or, k-anonymity meets differential privacy. CoRR , abs/1101.2604, 2011. [41] T. Li and N. Li. On the tradeoff between privacy and utility in data publishing. In International Conference on Knowledge Discovery and Data Mining , pages 517–526, 2009. [42] M. Lichman. UCI machine learning repository. , 2013. [43] M. R. Fouad, K. Elbassioni, and E. Bertino. Towards a differentially private data anonymization. CERIAS Tech Report 2012-1, Purdue Univ., 2012. [44] F. McSherry and K

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Decision Support System for Mitigating Athletic Injuries

:chronic workload ratio permits clinicians to quantify a player’s risk of subsequent injury. British Journal of Sports Medicine, 50, 471-475. Biermann, A. (1987). Fundamental mechanisms in machine learning and inductive inference: Part 2. Advanced Topics in Artificial Intelligence 345, 125-145. Bittencourt, N., Meeuwise, W., Mendonca, L., Nettel-Aguirre, A., Ocarino, L., & Fonseca, S. (2016). Complex systems approach for sports injuries: Moving from risk factor identification to injury pattern recognition – narrative review and new concept. British Journal of

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Privacy Protection for Audio Sensing Against Multi-Microphone Adversaries

References [1] [n. d.]. Amazon Alexa Premium Far-Field Voice Development Kit. . ([n. d.]). [2] [n. d.]. Apple Ipad Tech Specs. . ([n. d.]). [3] [n. d.]. HomePod reinvents music in the home. . ([n. d.]). [4] [n. d.]. Pixel 2 Tech Specs. . ([n. d.]). [5] 2017. The Nielsen Total Audience

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