Abstract
Web service providers collect user behaviors, such as purchases or locations, and use this information to provide personalized content. While no provider can collect behavioral information across different service providers, the behaviors for all service providers are accumulated in a user’s terminal. If a provider could analyze these behaviors stored in the terminal, it could provide more valuable services to the user. There is a problem, however, in that sensitive user information would be revealed when the provider obtained behaviors related to other services. This sensitive information consists of the user’s behaviors and characteristic tendencies analyzed from the collected information. In this paper, we propose a model for preserving privacy, called ρ-tendency certainty, which considers breaches of privacy from collected information. We also propose a behavioral tendency obfuscation framework, which sends dummy queries to service providers in order to satisfy ρ-tendency certainty. Experimental results show that the proposed framework can satisfy ρ-tendency certainty with a few number of dummy queries and create dummies within 1 msec, thus the proposed framework is applicable to real services.
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References
Sullivan, G.R.: The User of Added Error to Avoid Disclosure in Microdata Releases, PhD thesis, Iowa State University (1989)
Pang, H.: Obfuscating the Topical Intention in Enterprise Text Search. In: 28th International Conference on Data Engineering, pp. 1168–1179 (2012)
Chor, B.: Private Information Retrieval. In: 36th Annual IEEE Symposium on Foundations of Computer Science, pp. 41–50 (1995)
Kushilevitz, E.: Replication Is Not Needed: Single Database, Computationally-Private Information Retrieval. In: 38th Annual Symposium on Foundations of Computer Science, pp. 364–373 (1997)
Kido, H., Yanagisawa, Y., Satoh, T.: An Anonymous Communication Technique using Dummies for Location-based Services. In: International Conference on Pervasive Services (ICPS 2005), pp. 88–97 (2005)
Lu, H., Jensen, C.S., Yiu, M.L.: PAD: Privacy-Area Aware, Dummy-Based Location Privacy in Mobile Services. In: Proc. MobiDE, pp. 16–23 (2008)
Song, D.X.: Practical Techniques for Searches on Encripted Data. In: IEEE Symposium on Security and Privacy, pp. 44–55 (2000)
Lindel, Y.: Privacy Preserving Data Mining. Journal of Criptology 15, 177–206 (2002)
Adar, E.: User 4xxxxx9: Anonymizing Query Logs. In: Query Log Analysis Workshop, WWW (2007)
Chaum, D.L.: Untraceable Electronic Mail, Return Addresses, and Digital Pseudonyms. Communications of the ACM 24, 84–90 (1981)
Machanavajjhala, A.: l-Diversity: Privacy Beyond k-Anonymity. In: 22th International Conference Data Engineering, pp. 24–35 (2006)
Li, M.: t-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: 23th International Conference Data Engineering, pp. 106–115 (2007)
Xiao, X.: m-Invariance: Towards Privacy Preserving Re-publication of Dynamic Datasets. In: International Conference on Management of Data, pp. 689–700 (2007)
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Furukawa, R., Takenouchi, T., Mori, T. (2013). Behavioral Tendency Obfuscation Framework for Personalization Services. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40173-2_24
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DOI: https://doi.org/10.1007/978-3-642-40173-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40172-5
Online ISBN: 978-3-642-40173-2
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