Spatio-temporal traces left behind by moving individuals are increasingly available. On the one hand, mining this kind of data is expected to produce interesting behavioral knowledge enabling novel classes of mobility applications; but on the other hand, due to the peculiar nature of position data, mining it creates important privacy concerns. Thus, studying privacy preserving data mining methods for mov- ing object data is interesting and challenging. In this paper, we address the problem of hiding sensi- tive trajectory patterns from moving objects databases. The aim is to modify the database such that a given set of sen- sitive trajectory patterns can no longer be extracted, while the others are preserved as much as possible. We provide the formal problem statement and show that it is NP-hard; so we devise heuristics and a polynomial sanitization al- gorithm. We discuss a possible attack to our model, that exploits the knowledge of the underlying road network, and we enhance our model to protect from this kind of attacks. Experimental results show the effectiveness of our proposal.