Abstract
Web search engines capitalize on, or lend themselves to, the construction of user interest profiles to provide personalized search results. The lack of transparency about what information is stored, how it is used and with whom it is shared, limits the perception of privacy that users have about the search service. In this paper, we investigate a technology that allows users to replace specific queries with more general but semantically similar search terms. Through the generalization of queries, user profile could become less precise and therefore more private, although evidently at the expense of a degradation in the accuracy of search results. In this work, we design and develop a tool of PrivacySearch that implements this principle in real practice. Our tool, developed as a browser plug-in for Google Chrome, enables users to generalize the queries sent to a search engine in an automated fashion, without the need for any kind of infrastructure or external databases, and in real time, according to simple and intuitive privacy criteria. Experimental results demonstrate the technical feasibility and suitability of our solution.
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Notes
- 1.
In online marketing terminology, conversion usually means the act of converting Web site visitors into paying customers.
- 2.
Users can also choose in which search engine the processed search will be carried out.
- 3.
The plug-in is available at https://chrome.google.com/webstore/detail/ecippblhocppaciehgckhfboegciekkf.
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Acknowledgments
Partial support to this work has been received from the European Commission (projects H2020-644024 “CLARUS” and H2020-700540 “CANVAS”), and the Spanish Government (projects TIN2014-57364-C2-1-R “Smart-Glacis” and TIN2016-80250-R “Sec-MCloud”). J. Parra-Arnau is the recipient of a Juan de la Cierva postdoctoral fellowship, IJCI-2016-28239, from the Spanish Ministry of Economy and Competitiveness.
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Rodrigo-Ginés, FJ., Parra-Arnau, J., Meng, W., Wang, Y. (2018). PrivacySearch: An End-User and Query Generalization Tool for Privacy Enhancement in Web Search. In: Au, M., et al. Network and System Security. NSS 2018. Lecture Notes in Computer Science(), vol 11058. Springer, Cham. https://doi.org/10.1007/978-3-030-02744-5_23
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