Abstract
Services in smart environments usually require personal information to customize their behavior for the specific needs of a user. Traditionally users express privacy preferences in precompiled policies to control which information is disclosed to services within smart environments. A limitation of policies is that they are hard to create and maintain when the potentially communicated information or the context that influences a disclosure decision are highly diverse and hard to predict. Managing privacy ad hoc, in the moment when a service requests personal information, circumvents those problems. A drawback of ad hoc privacy control is the increased privacy related user interaction during service usage. This can be balanced by an assistance that handles personal information dynamically based on context information influencing a disclosure decisions. In this paper we describe a simulation environment to evaluate a context based, data mining driven disclosure assistance and present related results.
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References
Altman, I.: The Environment and Social Behavior: Privacy, Personal Space, Territory, and Crowding. Cole Publishing Company, Monterey (1975)
Palen, L., Dourish, P.: Unpacking “privacy” for a networked world. In: CHI 2003: Proc. of the SIGCHI Conference on Human Factors in Computing Systems, pp. 129–136. ACM, New York (2003)
Langheinrich, M.: A privacy awareness system for ubiquitous computing environments. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 315–320. Springer, Heidelberg (2002)
W3C: The platform for privacy preferences 1.0 (P3P1.0) specification (April 2002), http://www.w3.org/TR/P3P/ (accessed December 12, 2008)
Cranor, L.F., Guduru, P., Arjula, M.: User interfaces for privacy agents. ACM Trans. Comput.-Hum. Interact. 13(2), 135–178 (2006)
Myles, G., Friday, A., Davies, N.: Preserving privacy in environments with location-based applications. IEEE Pervasive Computing 2(1), 56–64 (2003)
Prabaker, M., Rao, J., Fette, I., Kelley, P., Cranor, L., Hong, J., Sadeh, N.: Understanding and capturing people’s privacy policies in a people finder application. In: UBICOMP 2007: Workshop on UBICOMP Privacy (September 2007)
Clauß, S., Pfitzmann, A., Hansen, M.: Privacy-enhancing identity management. The IPTS Report 67, 8–16 (2002), http://ipts.jrc.ec.europa.eu/home/report/english/articles/vol67/IPT2E676.htm (accessed January 26, 2009)
Jendricke, U., Kreutzer, M., Zugenmaier, A.: Pervasive privacy with identity management. Technical Report 178, Institut für Informatik, Universität Freiburg (October 2002)
Maibaum, N., Sedov, I., Cap, C.H.: A citizen digital assistant for e-government. In: Traunmüller, R., Lenk, K. (eds.) EGOV 2002. LNCS, vol. 2456, pp. 284–287. Springer, Heidelberg (2002)
Lederer, S., Mankoff, J., Dey, A.K., Beckmann, C.P.: Managing personal information disclosure in ubiquitous computing environments. Technical Report UCB/CSD-03-1257, EECS Department, University of California, Berkeley (July 2003)
Lederer, S., Hong, J.I., Dey, A.K., Landay, J.A.: Personal privacy through understanding and action: Five pitfalls for designers. Personal Ubiquitous Computing 8(6), 440–454 (2004)
Bünnig, C.: Learning context based disclosure of private information. In: The Internet of Things & Services - 1st Intl. Research Workshop, Valbonne, France (September 2008)
Bünnig, C.: Smart privacy management in ubiquitous computing environments. In: Smith, M.J., Salvendy, G. (eds.) Human Interface, Part II, HCII 2009. LNCS, vol. 5618. Springer, Heidelberg (2009)
Eagle, N., Pentland, A.: Reality mining - machine perception and learning of complex social systems (2009), http://reality.media.mit.edu/ (accessed June 12, 2009)
Lederer, S., Mankoff, J., Dey, A.K.: Who wants to know what when? privacy preference determinants in ubiquitous computing. In: CHI 2003: Extended Abstracts on Human Factors in Computing Systems, pp. 724–725. ACM, New York (2003)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: Yale: Rapid prototyping for complex data mining tasks. In: Proc. of the 12th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD 2006), pp. 935–940. ACM, New York (2006)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Cohen, W.W.: Fast effective rule induction. In: ICML, pp. 115–123 (1995)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco (1993)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf (accessed June 12, 2009)
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Bünnig, C. (2009). Simulation and Analysis of Ad Hoc Privacy Control in Smart Environments. In: Tavangarian, D., Kirste, T., Timmermann, D., Lucke, U., Versick, D. (eds) Intelligent Interactive Assistance and Mobile Multimedia Computing. IMC 2009. Communications in Computer and Information Science, vol 53. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10263-9_27
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DOI: https://doi.org/10.1007/978-3-642-10263-9_27
Publisher Name: Springer, Berlin, Heidelberg
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