A Review Paper on RASP Data Perturbation for Confidential and Efficient Queries in the Cloud

Abstract: Cloud computing is an emerging technology, Privacy and confidentiality has become the major concern in the public cloud. Data owners do not want to move their data to the cloud until and unless the confidentiality and the query privacy are preserved. On the other hand a secured query services should provide efficient query processing and reduce the in-house workload to get the total benefit of cloud computing. This paper presents a Random space perturbation method to provide secure and efficient range query and K nearest neighbor query services for protecting data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. Keywords: Privacy, Confidentiality, Range query, Knn query.