Shear-based spatial transformation to protect proximity attack of cloud data

Abstract

Due to the rapid advancement of location based services (LBS), the spatial data has been increased dramatically. Consequently, cloud computing has boost up its importance. Nowadays it is a common practice to upload data into the third party service provider. However, the most important challenge in cloud data is how to meet the privacy requirements and guarantee the integrity of the query result as well. Unfortunately, until now most of existing techniques couldn't support proper data privacy with reasonable execution cost. To carry on data privacy with rational execution cost for the cloud spatial data, we put forward spatial transformation that use shear transformation with rotation and shifting. We describe most important attack model measuring the data privacy of our transformation scheme. In addition, we devise a technique to evaluate the execution cost by the spatial range query. Finally, extensive experiments have demonstrated that our method has excellent performance against attack model for the data privacy with low communication cost.