  Recommendation systems and content filtering approaches based on annotations
and ratings, essentially rely on users expressing their preferences and
interests through their actions, in order to provide personalised content. This
activity, in which users engage collectively, has been named social tagging.
Although it has opened a myriad of new possibilities for application
interoperability on the semantic web, it is also posing new privacy threats.
Social tagging consists in describing online or online resources by using
free-text labels (i.e. tags), therefore exposing the user's profile and
activity to privacy attacks. Tag forgery is a privacy enhancing technology
consisting of generating tags for categories or resources that do not reflect
the user's actual preferences. By modifying their profile, tag forgery may have
a negative impact on the quality of the recommendation system, thus protecting
user privacy to a certain extent but at the expenses of utility loss. The
impact of tag forgery on content-based recommendation is, therefore,
investigated in a real-world application scenario where different forgery
strategies are evaluated, and the consequent loss in utility is measured and
compared.
