In this paper Cloud computing infrastructures are popularly used by peoples now a days. By using cloud users can save their cost for query services. But some of the data owners are hesitate to put their data’s in cloud because, sometimes the data may be hack when they use in cloud unless the confidentiality of data and secure query processing will be provided by the cloud provider.However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. The random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected 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. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries.

A Rebekah Johnson,N.Parashuram,Dr S.Prem Kumar."Building Confidential & Efficient Query Services in the Cloud with RASP Perturbation". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.2, Issue 09,pp.627-630, SEPTEMBER - 2015, URL :https://ijcert.org/ems/ijcert_papers/V2I920.pdf,

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