On Content-Based Recommendation and User Privacy in Social-Tagging Systems

TL;DR

This paper investigates how tag forgery as a privacy protection method affects the quality of content-based recommendations in social-tagging systems, analyzing different strategies and their utility trade-offs.

Contribution

It provides an empirical analysis of the impact of various tag forgery strategies on recommendation quality and privacy in real-world social-tagging applications.

Findings

Tag forgery can effectively enhance user privacy.

Different forgery strategies vary in their impact on recommendation utility.

There is a measurable trade-off between privacy protection and recommendation accuracy.

Abstract

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.