The rapid growth of the Web has led to the development of new solu- tions in the Web recommender or personalization domain, aimed to assist users in satisfying their information needs. The main goal of this chapter is to survey some of the recommender system proposals appeared in the literature, and to evaluate these pro- posals from the point of view of privacy preservation. Then, as an ex- ample of privacy-preserving approach for recommendations, we present ?SUGGEST, a privacy-enhanced system that allows for creating serendip- ity recommendations without breaching users privacy. ?SUGGEST helps users to navigate though a Web site, by providing dynamically generated links to relevant pages that have not yet been visited. The knowledge base on which the model used for making recommendations is built, is incrementally updated without tracking user sessions. This feature is par- ticularly important when users do not trust the system, and do not want disclose their complete activity records or preferences. In this case, users may adopt techniques that avoid server-based session reconstruction, and that do not worsen the accuracy of the model extracted by ?SUGGEST. As an additional contribution, we show that ?SUGGEST does not allow malicious users to track or detect users activity or preferences.