Science and Development Network (SciDev.Net)
Recommendation systems are information-filtering systems that help users deal with information overload. Unfortunately, current recommendation systems prompt serious privacy concerns. In this paper, the authors propose an architecture that enables users to enhance their privacy in those systems that profile users on the basis of the items rated. Their approach capitalizes on a conceptually-simple per-turbative technique, namely the suppression of ratings. In their scenario, users rate those items they have an opinion on. However, in order to avoid being accurately profiled, they may want to refrain from rating certain items. Consequently, this technique protects user privacy to a certain extent, but at the cost of degradation in the accuracy of the recommendation.