Abstract
Privacy protection in web search engines is becoming more and more serious in recent days. In this paper, we study the problem of privacy protection in web search, with a special focus on IP-address based personalized web search. Our goal is to break the linkage between users’ identities (e.g., IP address) and their issued queries so as to prevent privacy breaches. Our privacy model, which shares similar characteristics of l-diversity in privacy preserving data publishing of relational data, provides a strong privacy guarantee in web search. The central idea of our privacy model is to protect user’s search activities within a social peer group. A social peer group contains a set of individual users. From search engines’s perspective, search queries issued by users from the same peer group cannot be uniquely linked to individuals within the group. A framework based on grouping social peer users is proposed to achieve the privacy requirement. We also provide some experimental results to show that our methods achieve high efficiency in practice.
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References
Aggarwal, G., Feder, T., Kenthapadi, K., Khuller, S., Panigrahy, R., Thomas, D., Zhu, A.: Achieving anonymity via clustering. In: Proceedings of the 25th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2006), pp. 153–162. ACM, New York (2006)
Blundo, C.: Private information retrieval. In: Encyclopedia of Cryptography and Security, 2nd edn., pp. 974–976 (2011)
Bornhorst, N., Pesavento, M., Gershman, A.B.: Distributed beamforming for multiuser peer-to-peer and multi-group multicasting relay networks. In: ICASSP, pp. 2800–2803 (2011)
Clark, J., van Oorschot, P.C., Adams, C.: Usability of anonymous web browsing: an examination of tor interfaces and deployability. In: Proceedings of the 3rd Symposium on Usable Privacy and Security (SOUPS 2007), pp. 41–51. ACM, New York (2007)
Gkoulalas-Divanis, A., Verykios, V.S.: Hiding sensitive knowledge without side effects. Knowl. Inf. Syst. 20(3), 263–299 (2009)
Jones, R.: Privacy in web search query log mining. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part I. LNCS (LNAI), vol. 5781, p. 4. Springer, Heidelberg (2009)
Kim, Y., Sohn, S.Y.: Stock fraud detection using peer group analysis. Expert Syst. Appl. 39(10), 8986–8992 (2012)
Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD 2008), pp. 93–106. ACM Press, New York (2008)
Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: L-diversity: Privacy beyond k-anonymity. In: Proceedings of the 22nd IEEE International Conference on Data Engineering (ICDE 2006). IEEE Computer Society, Washington, DC (2006)
Murugesan, M., Clifton, C.: Providing privacy through plausibly deniable search. In: Proceedings of the SIAM International Conference on Data Mining (SDM 2009), pp. 768–779. SIAM (2009)
Pang, H., Ding, X., Xiao, X.: Embellishing text search queries to protect user privacy. PVLDB 3(1), 598–607 (2010)
Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Transactions on Knowledge and Data Engineering (TKDE) 13(6), 1010–1027 (2001)
Shtykh, R.Y., Zhang, G., Jin, Q.: Peer-to-peer solution to support group collaboration and information sharing. Int. J. Pervasive Computing and Communications 1(3), 187–198 (2005)
Sweeney, L.: K-anonymity: a model for protecting privacy. International Journal on uncertainty, Fuzziness and Knowledge-based System 10(5), 557–570 (2002)
Tsuneizumi, I., Aikebaier, A., Enokido, T., Takizawa, M.: A scalable peer-to-peer group communication protocol. In: AINA, pp. 268–275 (2010)
http://www.ntia.doc.gov/legacy/ntiahome/privacy/files/smith.htm
Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE 2008), pp. 506–515. IEEE Computer Society, Cancun (2008)
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Zhou, B., Xu, J. (2013). Privacy Protection in Personalized Web Search: A Peer Group-Based Approach. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_46
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DOI: https://doi.org/10.1007/978-3-642-37210-0_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37209-4
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