Nowadays,　many　practical　applications　need　to　publish　streaming　data　continuously.　Most　of　existing　research　works　for　differential　privacy　single　streaming　data　publication　focus　on　range　accumulation.　However,　many　practical　scenarios　need　to　answer　arbitrary　range　counting　queries　of　streaming　data.　At　the　same　time,　there　exist　specific　rules　of　queries　from　users,　so　adaptive　analysis　and　calculation　for　historical　queries　should　be　concerned.　To　improve　the　usability　of　published　data,　an　algorithm　HQ_DPASP　for　differential　privacy　streaming　data　adaptive　publication　based　on　historical　queries　is　proposed.　Combining　the　characteristics　of　streaming　data,　HQ_DPASP　firstly　uses　the　sliding　window　mechanism　to　construct　the　differential　privacy　range　tree　of　the　streaming　data　dynamically.　Secondly,　by　analyzing　the　coverage　probability　of　tree　nodes　and　calculating　the　privacy　parameters　from　leaves　to　root,　HQ_DPASP　allocates　privacy　budget　from　root　to　leaves　and　adds　non-uniform　noise　on　tree　nodes.　Finally,　the　privacy　budget　of　tree　nodes　and　tree＇s　parameters　are　adjusted　adaptively　based　on　the　characteristic　of　historical　queries.　Experiments　are　designed　for　testing　the　feasibility　and　effectiveness　of　HQ_DPSAP.　The　results　show　that　HQ_DPSAP　is　effective　in　answering　arbitrary　range　counting　queries　on　the　published　streaming　data　while　assuring　low　mean　squared　error　of　queries　and　high　algorithm　efficiency.　©　2017,　Science　Press.　All　right　reserved.