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.