The increasing availability of space-time trajectories left by location-aware devices is expected to enable novel classes of applications where the discovery of consumable, concise, and actionable knowledge is the key step. However, the analysis of mobility data is a critic task by the privacy point of view: in fact, the peculiar nature of location data might enable intrusive inferences in the life of the individuals whose data is analyzed. It is thus important to develop privacy-preserving techniques for the publication and the analysis of mobility data.

This chapter provides a brief survey of the research on anonymity preserving data publishing of moving objects databases.

While only few papers so far have tackled the problem of anonymity in the off-line case of publication of a moving objects database, rather large body of work has been developed for anonymity on relational data on one side, and for location privacy in the on-line, dynamic context of location based services (LBS), on the other side. In this chapter we first briefly review the basic concepts of k-anonymity on relational data. Then we focus on the body of research about privacy in LBS: we try to identify some useful concepts for our static context, while highlighting the differences, and discussing the inapplicability of some of the LBS solutions to the static case. Next we present in details some of the papers that recently have attacked the problem of moving objects anonymization in the static context. We discuss in details the problems addressed and the solutions proposed, highlighting merits and limits of each work, as well as the various problems still open.