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.
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
Similar content being viewed by others
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.
References
Cao, B., Sun, J.-T., Xiang, E.W., Hu, D.H., Yang, Q., Chen, Z.: PQC: Personalized query classification. In: ACM Eighteenth Conference on Information and Knowledge Management, pp. 1217-1226 (2017)
Aktolga, E., Jain, A., Velipasaoglu, E.: Building rich user search queries profiles. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 254–266. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38844-6_21
Ortiz-Cordova, A., Jansen, B.J.: Classifying web search queries to identify high revenue generating customers. J. Assoc. Inf. Sci. Technol. 63(7), 426–1441 (2012)
Zetter, K.: Yahoo issues takedown notice for spying price list. In: Wired (2009). https://www.wired.com/2009/12/yahoo-spy-prices/
Pariser, E.: El filtro burbuja: Cómo la web decide lo que leemos and lo que pensamos. Penguin Random House Grupo Editorial (2017)
European Commission: Media pluralism and democracy: outcomes of the 2016 Annual Colloquium on Fundamental Rights. In: Annual Colloquium on Fundamental Rights, pp. 14–15 (2016)
Purcell, K., Brenner, J., Rainie, L.: Search engine use 2012. In: Pew Internet (2012)
Penn, M.: Views from Around the Globe: 2nd Annual Poll on How Personal Technology is Changing Our Lives (2015)
Mozilla Foundation: Online Privacy & Security Survey (2017)
Sánchez, D., Castellà-Roca, J., Viejo, A.: Knowledge-based scheme to create privacy-preserving but semantically-related queries for web search engines. Inf. Sci. 218, 17–30 (2013)
Shen, X., Tan, B., Zhai, C.: Privacy protection in personalized search. In: ACM SIGIR Forum, vol. 41, no. 1 (2007)
Howe, D.C., Nissenbaum, H.: TrackMeNot: resisting surveillance in web search. Lessons Identity Trail: Priv. Anonymity Identity Netw. Soc. 290, 417–436 (2006)
Chow, R., Golle, P.: Faking contextual data for fun, profit, and privacy. In: Proceedings of the 8th ACM Workshop on Privacy in the Electronic Society, pp. 105-109 (2009)
Domingo-Ferrer, J., Solanas, A., Castellà-Roca, J.: h (k)-Private information retrieval from privacy-uncooperative queryable databases. Online Inf. Rev. 33(4), 720–744 (2009)
Balsa, E., Troncoso, C., Díaz, C.: OB-PWS: obfuscation-based private web search. In: IEEE Symposium on Security and Privacy (SP), pp. 491–505. IEEE (2012)
Xu, Y., Wang, K., Zhang, B., Chen, Z.: Privacy-enhancing personalized web search. In: Proceedings of the 16th International Conference on World Wide Web, pp. 591–600 (2007)
Rebollo-Monedero, D., Parra-Arnau, J., Forné, J.: An Information-Theoretic Privacy Criterion for Query Forgery in Information Retrieval. In: Kim, T., Adeli, H., Fang, W., Villalba, J.G., Arnett, K.P., Khan, M.K. (eds.) SecTech 2011. CCIS, vol. 259, pp. 146–154. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-27189-2_16
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Snasel, V., Moravec, P., Pokorny, J.: WordNet ontology based model for web retrieval. In: Web Information Retrieval and Integration, pp. 220–225 (2005)
Stevenson, M., Wilks, Y.: Word-sense disambiguation. In: The Oxford Handbook of Computational Linguistics, pp. 249–265 (2003)
Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation SIGDOC 1986, pp. 24–26 (1986)
Scott, S., Matwins, S.: Text classification using WordNet hypernyms. In: Usage of WordNet in Natural Language Processing Systems (1998)
Ganesan, P., Garcia-Molina, H., Widom, J.: Exploiting hierarchical domain structure to compute similarity. ACM Trans. Inf. Syst. (TOIS) 21(1), 64–93 (2003)
Mansuy, T., Hilderman, R.J.: A characterization of WordNet features in Boolean models for text classification. In: Proceedings of the Fifth Australasian Conference on Data Mining and Analytics, vol. 61 (2006)
Arrington, M.: AOL proudly releases massive amounts of private data. In: TechCrunch (2006). www.techcrunch.com/2006/08/06/aol-proudly-releases-massive-amounts-of-user-search-data
Silverstein, C., Henzinger, M., Marais, H., Moricz, M.: Analysis of a very large web search engine query log. ACm SIGIR Forum 33(1), 6–12 (1999)
Dou, Z., Song, R., Wen, J.-R.: A large-scale evaluation and analysis of personalized search strategies. In: Proceedings of the 16th International Conference on World Wide Web, pp. 581–590 (2007)
Reiter, M., Rubin, A.D.: Crowds: anonymity for Web transactions. ACM Trans. Inf. Syst. Secur. I, 66–92 (1998)
Beales, H.: The value of behavioral targeting, Tech. rep., Netw. Advertising Initiative, March 2010. http://www.networkadvertising.org/pdfs/Beales_NAI_Study.pdf. Accessed 15 Jan 2016
Avi, A., Efraimidis, P.S., Drosatos, G.: A query scrambler for search privacy on the internet. Inf. Retr. 16(6), 657–679 (2013)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-02744-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02743-8
Online ISBN: 978-3-030-02744-5
eBook Packages: Computer ScienceComputer Science (R0)