Most　existing　methods　for　differential　privacy　streaming　data　statistical　release　based　on　tree　structures　cannot　take　advantage　of　the　special　probability　distributions　in　statistical　queries.　This　study　presents　an　algorithm　that　further　boosts　the　accuracy　of　the　released　streaming　data　for　differential　privacy　streaming　data　publication　with　non-uniform　private　budgets　using　sliding　windows.　After　constructing　the　differential　privacy　range　tree　for　the　streaming　data　within　the　sliding　window,　the　algorithm　calculates　the　coverage　probability　of　the　tree　nodes　according　to　the　probability　distribution　of　the　statistical　queries　and　then　adds　non-uniform　noise　to　the　tree　nodes　based　on　the　adjusted　privacy　budget　of　the　tree　nodes　and　a　tree　structure　parameter.　Finally,　several　real-time　adjustment　policies　are　designed　to　ensure　that　the　node　values　on　any　path　from　the　root　to　the　leaves　satisfy　a　consistency　constraint.　Tests　show　that　the　algorithm　guarantees　better　statistical　query　accuracy　and　has　higher　algorithm　efficiency　than　traditional　algorithms.　©　2019,　Tsinghua　University　Press.　All　right　reserved.