Recently, privacy preserving trajectory data publishing has become a hot topic in data privacy preserving research fields. Most previous works on privacy preserving trajectory data publishing adopt clustering techniques. However, clustering based algorithms for trajectory data publishing only consider preserving the privacy of each single trajectory, ignoring the protection of the characteristics of trajectory clustering groups. Therefore, the publishing trajectory data by clustering are vulnerable to suffer re-clustering attacks, which is verified by theoretical analysis and simulated experiments. In order to avoid re-clustering attacks, a (k,δ,Δ)-anonymity model and a clustering hybrid based algorithm CH-TDP for privacy preserving trajectory data publishing are presented. The key idea of CH-TDP is to firstly hybridize between clustering groups, which are generated by the (k,δ)-anonymity model and the related algorithms, and then adopt perturbation within each clustering group. The aim of CH-TDP is to avoid suffering re-clustering attacks effectively while assuring the data quality of the released trajectory data not less than a threshold Δ. CH-TDP and the traditional algorithms are compared and experimental results show that CH-TDP is effective and feasible.