Spatial　range　query　enjoys　widespread　application　scenarios　due　to　the　ever-growing　geo-positioning　technology　in　recent　years.　Huge　amounts　of　encrypted　geo-location　data　are　being　outsourced　to　cloud　servers　to　alleviate　local　storage　and　computational　overheads　without　leaking　sensitive　information.　However,　most　existing　Privacy-preserving　Spatial　Range　Query　(PSRQ)　cannot　achieve　high　efficiency　while　satisfying　strong　security　over　large-scale　encrypted　spatial　data.　To　strike　a　best-possible　balance　between　security　and　efficiency,　we　propose　a　novel　efficient　Privacy-preserving　Spatial　Range　Query　(ePSRQ)　scheme　in　dual-cloud　architecture　over　large-scale　dataset.　Specifically,　we　propose　an　efficient　PSRQ　scheme　by　designing　a　novel　index　structure　based　on　Geohash　algorithm,　Circular　Shift　and　Coalesce　Zero-Sum　Garbled　Bloom　Filter　(CSC-ZGBF)　and　Symmetric　Homomorphic　Encryption　(SHE),　which　makes　the　computational　complexity　of　query　process　independent　of　dataset　size.　Formal　security　analysis　proves　that　our　scheme　can　achieve　Indistinguishability　against　Chosen-Plaintext　Attack　(IND-CPA),　and　extensive　experiments　prove　that　our　scheme　is　feasible　in　real-world　applications.