Spatial data is freely available as part of production and geographic information. Spatial Data Mining (SDM) is a way to mine new information from large spatial data sets. Large volumes of spatial data come from various domains, such as agriculture, medical, and educational sources. The SDM technique is designed to work with organized relationships to discover patterns from large set of data. Data which is correlated to business or market is called big data. It is a term for mixture of information when its volume, variability and velocity create challenges in technologies or tools like relational databases. So there is a demand to analyze the data with the help of parallel processing using Hadoop cloud framework. This paper defines the spatial Hadoop cloud system with efficient indexing and spatial operations with privacy preserving scheme for cloud storage. The core system contains traditional spatial indexing scheme with enhanced scheme that includes R tree, Hilbert R tree, and enhanced Bloom filter tree. We improve the query search using Spatial Join, Range query, KNN and Max KNN queries. With the ubiquity of outsourced data and services to encrypted cloud and using Multi-authority attribute based encryption, we show that our. proposed system is more efficient in terms of data retrieval, data encryption and search in spatial cloud. Copyright © 2018 American Scientific Publishers All rights reserved.