Advances in positioning technologies together with the wide adoption of GPS-enabled smartphones enable accurate and low-cost tracking of user location. This allows the collection of large amounts of person-specific mobility data that offer remarkable opportunities for data analysis. Yet, the sharing of such data poses significant privacy risks. This enunciates the need for privacy-preserving, trajectory data publishing methods. Existing approaches are either limited in their privacy specification component or they incur significant, and often unnecessary, data distortion. In response, we propose a novel framework for anonymizing trajectory data that prevents the disclosure of both identity and sensitive location information, while retaining data utility. Our framework involves: (i) selecting similar trajectories, by employing Z-ordering or data projections on frequent sub trajectories, (ii) organizing the selected trajectories into carefully constructed clusters, and (ii) anonymizing each cluster separately. We develop algorithms to realize our framework, which are effective and efficient, as verified by extensive experiments. © 2013 IEEE.