Recently,　spatial　keyword　query　services　have　been　widely　deployed　in　real-life　applications,　such　as　location-based　services　and　social　networking.　Several　privacy-preserving　spatial　keyword　queries　solutions　were　proposed　to　guarantee　data　security　and　query　privacy　on　outsourced　data.　However,　those　solutions　are　either　based　on　broken　cryptographic　tools　or　support　a　single　query　type,　and　hence　cannot　meet　the　security　and　functionality　requirements　in　practical　applications.　In　this　paper,　we　propose　a　Secure　Spatial　Keyword　Queries　(SSKQ)　construction　supporting　expressive　query　types.　Specifically,　we　present　a　secure　index　structure　for　spatial-textual　data　based　on　the　encrypted　Quadtree　and　Bloom　filter,　which　can　prune　the　index　tree　dynamically　and　only　reveal　the　files　associated　with　a　set　of　keywords.　The　security　analysis　and　the　experiments　conducted　on　real-world　datasets　demonstrate　the　security　and　performance　of　our　construction.　©　2021　IEEE.