Privacy-Preserving Enhanced Collaborative Tagging

Abstract

Collaborative tagging is one of the most popular and dif-fused services available online. The main purpose of collaborative tagging is to loosely classify resources based on end-users feedback, expressed in the form of tags. Con-tent/resource categorization has been seen a challenging research topic in recent year. Tag suppression is a privacy enhancing technique for the semantic Web. In this paper, users are assigned a tag to resources on the Web revealing their personal preferences. However, in order to avoid privacy attackers from profiling users based on their interests, they may wish to refrain from tagging certain resources. Consequently, tag suppression protects user privacy to a certain manner, but at the cost of semantic loss incurred by suppressing tags. In a nutshell, this technique poses a trade-off between privacy and suppression. In this paper, this trade off is investigated in a systematic fashion and provides an extensive theoretical analysis. User privacy is measure as the entropy of the users tag distribution after the suppression of some tags