Recommendation systems are information-filtering systems that tailor information to users on the basis of knowledge about their preferences. The ability of these systems to profile users is what enables such intelligent functionality, but at the same time, it is the source of serious privacy concerns. In this paper we investigate a privacy-enhancing technology that aims at hindering an attacker in its efforts to accurately profile users based on the items they rate. Our approach capitalizes on the combination of two perturbative mechanisms—the forgery and the suppression of ratings. While this technique enhances user privacy to a certain extent, it inevitably comes at the cost of a loss in data utility, namely a degradation of the recommendation’s accuracy. In short, it poses a trade-off between privacy and utility. The theoretical analysis of such trade-off is the object of this work. We measure privacy as the Kullback-Leibler divergence between the user’s and the population’s item distributions, and quantify utility as the proportion of ratings users consent to forge and eliminate. Equipped with these quantitative measures, we find a closed-form solution to the problem of optimal forgery and suppression of ratings, an optimization problem that includes, as a particular case, the maximization of the entropy of the perturbed profile. We characterize the optimal trade-off surface among privacy, forgery rate and suppression rate,and experimentally evaluate how our approach could contribute to privacy protection in a real-world recommendation system.