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. © 2013 Springer-Verlag.
CITATION STYLE
Arampatzis, A., Drosatos, G., & Efraimidis, P. S. (2013). A versatile tool for privacy-enhanced web search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7814 LNCS, pp. 368–379). https://doi.org/10.1007/978-3-642-36973-5_31
Mendeley helps you to discover research relevant for your work.