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Hamza Harkous, Rameez Rahman, Bojan Karlas and Karl Aberer

),” Tech. Rep. MSR-TR-2015-37, May 2015. [13] M. Harbach, M. Hettig, S. Weber, and M. Smith, “Using personal examples to improve risk communication for security & privacy decisions,” in Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems, ser. CHI ’14. New York, NY, USA: ACM, 2014, pp. 2647-2656. [14] H. Harkous, R. Rahman, and K. Aberer, “C3p: Contextaware crowdsourced cloud privacy,” in Privacy Enhancing Technologies. Springer, 2014, pp. 102-122. [15] T. Hothorn, F. Bretz, and P. Westfall

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Susan E. McGregor, Franziska Roesner and Kelly Caine

/status/448105177439285248. [21] P. Thornton, “Outlook/Exchange vs. GMAIL,” The Journalism Iconoclast, May 2008. [Online]. Available: [22] N. Borisov, I. Goldberg, and E. Brewer, “Off-the-record communication, or, why not to use PGP,” in ACM Workshop on Privacy in the Electronic Society, 2004. [23] P. R. Zimmermann, The Official PGP User’s Guide. Cambridge, MA, USA: MIT Press, 1995. [24] R. Dingledine, N. Mathewson, and P. Syverson, “Tor: The second

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Dan Bogdanov, Liina Kamm, Baldur Kubo, Reimo Rebane, Ville Sokk and Riivo Talviste

Felipe Saint-Jean. Secure computation of surveys. In EU Workshop on Secure Multiparty Protocols, 2004. [15] Liina Kamm. Privacy-preserving statistical analysis using secure multi-party computation. PhD thesis, University of Tartu, 2015. [16] Sven Laur, Riivo Talviste, and Jan Willemson. From Oblivious AES to Efficient and Secure Database Join in the Multiparty Setting. In Proceedings of ACNS’13, volume 7954 of LNCS, pages 84-101. Springer, 2013. [17] Latanya Sweeney. k-anonymity: a model for protecting privacy

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A. Geetha Mary, D. P. Acharjya and N. Ch. S. N. Iyengar

References 1. Sweeney, L. Computational Disclosure Control: A Primer on Data Privacy Protection. Ph.D. Thesis. Massachusetts Institute of Technology, 2001. 2. Sweeney, L. Achieving k-Anonymity Privacy Protection Using Generalization and Suppression. - International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 10, 2002, No 5, 571-588. 3 . Machanavajjhala, A., D. Kifer, J. Gehrke, M. Venkitasubramani am. L-Diversity: Privacy Beyond K-Anonymity. - ACM Trans. Knowl. Discov. Data, Vol. 1, 2007

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Qatrunnada Ismail, Tousif Ahmed, Kelly Caine, Apu Kapadia and Michael Reiter

References [1] Yuvraj Agarwal and Malcolm Hall. 2013. ProtectMyPrivacy: detecting and mitigating privacy leaks on iOS devices using crowdsourcing. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services . ACM, 97–110. [2] Shahriyar Amini, Jialiu Lin, Jason I Hong, Janne Lindqvist, and Joy Zhang. 2013. Mobile Application Evaluation Using Automation and Crowdsourcing. In Workshop on Privacy Enhancing Tools . [3] Karissa Bell. 2015. The 7 best iPhone photography apps of all time. (2015). http

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Christoph Bösch, Benjamin Erb, Frank Kargl, Henning Kopp and Stefan Pfattheicher

. Agarwal, “Your location has been shared 5,398 times!: A field study on mobile app privacy nudging,” in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, ser. CHI ’15. New York, NY, USA: ACM, 2015, pp. 787-796. [7] Y. Amichai-Hamburger and E. Ben-Artzi, “Loneliness and internet use,” Computers in Human Behavior, vol. 19, no. 1, pp. 71-80, 2003. [8] C. M. Angst and R. Agarwal, “Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual

Open access

P. Gayathiri and B. Poorna

Mining by Adding Items. – ACM, July 2014, pp. 65-66. 4. Cheng, P., J.-S. Pan. Completely Hide Sensitive Association Rules Using EMO by Deleting Transactions. – ACM, July 2014, pp. 167-168. 5. Cheng, P., C.-W. Lin, J.-S. Pan. Use HypE to Hide Association Rules by Adding Items. – PLOS ONE | DOI:10.1371/journal.pone.0127834, Vol. 10 , 12 June 2015, Issue 6, pp. 1-19. 6. Lin, C.-W., T.-P. Hong, H.-C. H s u. Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining. – Hindawi Publishing Corporation, The Scientific World Journal

Open access

Wisam Eltarjaman, Rinku Dewri and Ramakrishna Thurimella

Geo-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

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Berker Ağır, Kévin Huguenin, Urs Hengartner and Jean-Pierre Hubaux

. [11] R. Shokri, G. Theodorakopoulos, G. Danezis, J.-P. Hubaux, and J.-Y. Le Boudec, “Quantifying Location Privacy: The Case of Sporadic Location Exposure,” in Proc. of the Privacy Enhancing Technologies Symp. (PETS), 2011. [12] A.-M. Olteanu, K. Huguenin, R. Shokri, M. Humbert, and J.-P. Hubaux, “Quantifying interdependent privacy risks with location data,” IEEE Transactions on Mobile Computing, p. 14, 2016, to appear. [13] H. Liu, B. Luo, and D. Lee, “Location type classification using tweet content,” in Proc. of the Int’l Conf. on

Open access

Emmanuel Bello-Ogunu and Mohamed Shehab

References [1] Helen Nissenbaum. Privacy as contextual integrity. Washington law review, 79(1), 2004. [2] Galen Grumen. What you need to know about using bluetooth beacons, 2014. [3] Cathy Goodwin. A conceptualization of motives to seek privacy for nondeviant consumption. Journal of Consumer Psychology, 1(3):261-284, 1992. [4] Bluetooth SIG. Bluetooth smart technology: Powering the