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Tales from the Dark Side: Privacy Dark Strategies and Privacy Dark Patterns

References [1] G. Acar, C. Eubank, S. Englehardt, M. Juarez, A. Narayanan, and C. Diaz, “The Web never forgets: Persistent tracking mechanisms in the wild,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2014, pp. 674-689. [2] A. Acquisti, “Privacy in electronic commerce and the economics of immediate gratification,” in Proceedings of the 5th ACM conference on Electronic commerce. ACM, 2004, pp. 21-29. [3] -, “Nudging privacy: The behavioral economics of personal

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Choosing Epsilon for Privacy as a Service

References [1] J. M. Abowd and I. Schmutte. Revisiting the economics of privacy: Population statistics and confidentiality protection as public goods. Document 22, Labor Dynamics Institute, Jan. 2015. [2] J. Brodkin. At&t offers gigabit internet discount in exchange for your web history., Posted: 12/11/2013. [3] Y. Chen, S. Chong, I. A. Kash, T. Moran, and S. Vadhan. Truthful mechanisms for agents that value privacy. In

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Cross-Cultural Privacy Prediction

References [1] A. Acquisti and J. Grossklags. Privacy Attitudes and Privacy Behavior. In L. Camp and S. Lewis, editors, Economics of Information Security , volume 12 of Advances in Information Security , pages 165–178. Springer US, 2004. [2] I. Ajzen. The theory of planned behavior. Organizational Behavior and Human Decision Processes , 50(2):179–211, 1991. [3] I. Altman. Privacy Regulation: Culturally Universal or Culturally Specific? Journal of Social Issues , 33(3):66–84, July 1977. [4] A. Anton, J. Earp, and J. Young. How internet

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Privacy Loss Classes: The Central Limit Theorem in Differential Privacy

References [1] “CoverUp Measurement Data,” , 2018, [Online]. [2] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep Learning with Differential Privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2016, pp. 308–318. [3] M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1st ed. New York: Dover, 1972. [4] B. Balle, G. Barthe

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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] . [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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Oblivious DNS: Practical Privacy for DNS Queries

4033, March 2005. . [5] B ernstein , D. J. Curve25519: New Diffie-Hellman speed records. In Public Key Cryptography - PKC 2006 (Berlin, Heidelberg, 2006). [6] B ernstein , D. J. DNSCurve: Usable security for DNS. , 2009. [7] B ortzmeyer , S. DNS query name minimisation to improve privacy. RFC 7816, March 2016. [8] C alder , M., F lavel , A., K atz -B assett , E., M ahajan , R., and P adhye , J. Analyzing the performance of an any-cast cdn. In Proceedings of the 2015 Internet

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Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

References [1] A. Agresti. Categorical Data Analysis. Wiley Series in Probability and Statistics. Wiley-Interscience, 2nd edition, 2002. [2] Alexa. The top 500 sites on the web. [3] Jane R Bambauer, Krish Muralidhar, and Rathindra Sarathy. Fool’s gold: an illustrated critique of differential privacy. 2013. [4] Raef Bassily and Adam Smith. Local, private, efficient protocols for succinct histograms. In STOC. ACM, June 2015, to appear. [5] T-H Hubert

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PrivateRide: A Privacy-Enhanced Ride-Hailing Service

: May 2016. [7] . Last visited: May 2016. [8] . Last visited: May 2016. [9] Cryptographic key length recommendation. . Last visited: May 2016. [10] G. Arfaoui, J.-F. Lalande, J. Traoré, N. Desmoulins, P. Berthomé, and S. Gharout. A Practical Set-Membership Proof for Privacy-Preserving NFC Mobile Ticketing. Proc. of the 15th Privacy Enhancing Technologies

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Encrypted Databases for Differential Privacy

References [1] Javallier. . [2] J. Abowd. The challenge of scientific reproducibility and privacy protection for statistical agencies., 15 September 2016. . [3] G. Acs and C. Castelluccia. A case study: privacy preserving release of spatio-temporal density in paris. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining , pages 1679–1688. ACM, 2014

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Why Privacy Is All But Forgotten
An Empirical Study of Privacy & Sharing Attitude

References [1] M. S. Ackerman, L. F. Cranor, and J. Reagle. Privacy in ecommerce: examining user scenarios and privacy preferences. In Proceedings of the 1st ACM conference on Electronic commerce , pages 1–8. ACM, 1999. [2] A. Acquisti and R. Gross. Imagined communities: Awareness, information sharing, and privacy on the facebook. In Privacy enhancing technologies , pages 36–58. Springer, 2006. [3] A. Acquisti and J. Grossklags. Privacy and rationality in individual decision making. IEEE Security & Privacy , 2:24–30, 2005. [4] I. Ajzen

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