Recently,　differential　privacy　data　publication　has　become　an　extremely　important　research　topic　in　data　security　field.　However,　most　of　the　differential　privacy　algorithms　do　not　take　sparse　data　publishing　into　consideration.　The　aim　of　this　study　is　to　present　an　effective　differential　privacy　algorithm　for　two-dimensional　sparse　data　publication,　so　as　to　boost　the　accuracy　of　range　queries　of　the　released　data.　The　proposed　approach　in　this　paper　includes　two　steps:　1)　getting　the　sampling　set　of　the　original　two-dimensional　sparse　dataset　by　adopting　filter　sampling　algorithm;　2)　building　an　incomplete　quadtree　based　on　the　sampling　dataset　and　adjusting　the　tree　nodes＇　noise　values　under　consistency.　Experimental　analysis　is　designed　by　comparing　the　proposed　algorithm　and　the　traditional　algorithms　on　the　accuracy　of　range　queries　in　the　released　data.　Experimental　results　show　that　the　proposed　algorithm　is　effective　and　feasible.