Abstract
We consider the problem of privacy leaks suffered by Internet users when they perform web searches, and propose a framework to mitigate them. Our approach, which builds upon and improves recent work on search privacy, approximates the target search results by replacing the private user query with a set of blurred or scrambled queries. The results of the scrambled queries are then used to cover the original user interest. We model the problem theoretically, define a set of privacy objectives with respect to web search and investigate the effectiveness of the proposed solution with a set of real queries on a large web collection. Experiments show great improvements in retrieval effectiveness over a previously reported baseline in the literature. Furthermore, the methods are more versatile, predictably-behaved, applicable to a wider range of information needs, and the privacy they provide is more comprehensible to the end-user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arampatzis, A., Efraimidis, P., Drosatos, G.: Enhancing Deniability against Query-Logs. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 117–128. Springer, Heidelberg (2011)
Barbaro, M., Zeller, T.: A Face Is Exposed for AOL Searcher No. 4417749 (2006), http://www.nytimes.com/2006/08/09/technology/09aol.html (accessed June 3, 2010)
Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: GSCL, pp. 31–40. Gunter Narr Verlag, Tbingen (2009)
Brown, P.F., Pietra, V.J.D., de Souza, P.V., Lai, J.C., Mercer, R.L.: Class-based n-gram models of natural language. Computational Linguistics 18(4), 467–479 (1992)
Callan, J.P., Connell, M.E.: Query-based sampling of text databases. ACM Trans. Inf. Syst. 19(2), 97–130 (2001)
Caprara, A., Toth, P., Fischetti, M.: Algorithms for the set covering problem. Annals of Operations Research 98, 353–371 (2000)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press (2008)
Pass, G., Chowdhury, A., Torgeson, C.: A picture of search. In: InfoScale. ACM (2006)
Solove, D.J.: Understanding Privacy. Harvard University Press (2008)
Sweeney, L.: k-Anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)
Terra, E.L., Clarke, C.L.A.: Frequency estimates for statistical word similarity measures. In: NAACL-HLT, pp. 165–172. ACL (2003)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: ICML, pp. 412–420. Morgan Kaufmann (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arampatzis, A., Drosatos, G., Efraimidis, P.S. (2013). A Versatile Tool for Privacy-Enhanced Web Search. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-36973-5_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
eBook Packages: Computer ScienceComputer Science (R0)