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, and it is one of the most popular opportunities for users to engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. In fact, it consists in describing online or offline resources by using free-text labels, i.e., tags, thereby exposing a user's profile and activity to privacy attacks. As a result, users may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy-enhancing technology consisting in generating tags for categories or resources that do not reflect the user's actual preferences too accurately. 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 isconsequently investigated in a real-world application scenario where different forgery strategies are evaluated, and the resulting loss in utility is measured and compared. 
On content-based recommendation and user privacy in social-tagging systems