Issue No. 01 - Jan. (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.248
Javier Parra-Arnau , Dept. of Telematics Eng., Univ. Polite`cnica de Catalunya, Barcelona, Spain
Andrea Perego , Inst. for Environ. & Sustainability, Eur. Comm.-Joint Res. Centre of the Eur. Comm., Ispra, Italy
Elena Ferrari , Dept. of Theor. & Appl. Sci., Univ. of Insubria, Varese, Italy
Jordi Forne , Dept. of Telematics Eng., Univ. Polite`cnica de Catalunya, Barcelona, Spain
David Rebollo-Monedero , Dept. of Telematics Eng., Univ. Polite`cnica de Catalunya, Barcelona, Spain
Collaborative tagging is one of the most popular services available online, and it allows end user to loosely classify either online or offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Although tags may not be per se sensitive information, the wide use of collaborative tagging services increases the risk of cross referencing, thereby seriously compromising user privacy. In this paper, we make a first contribution toward the development of a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Moreover, we analyze how our approach can affect the effectiveness of a policy-based collaborative tagging system that supports enhanced web access functionalities, like content filtering and discovery, based on preferences specified by end users.
Privacy, Collaboration, Entropy, Semantics, Tag clouds, Data privacy
J. Parra-Arnau, A. Perego, E. Ferrari, J. Forne and D. Rebollo-Monedero, "Privacy-Preserving Enhanced Collaborative Tagging," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 1, pp. 180-193, 2013.