Recommendation systems are information-filtering systems that help users deal with information overload. Unfortunately, current recommendation systems prompt serious privacy concerns. In this work, we propose an architec- ture that protects user privacy in such collaborative-filtering systems, in which users are profiled on the basis of their ratings. Our approach capitalizes on the combination of two perturbative techniques, namely the forgery and the suppres- sion of ratings. In our scenario, users rate those items they have an opinion on. However, in order to avoid privacy risks, they may want to refrain from rating some of those items, and/or rate some items that do not reflect their actual prefer- ences. On the other hand, forgery and suppression may degrade the quality of the recommendation system. Motivated by this, we describe the implementation de- tails of the proposed architecture and present a formulation of the optimal trade- off among privacy, forgery rate and suppression rate. Finally, we provide a nu- merical example that illustrates our formulation.