Building Trustworthy And Resourceful Interrogation Services In The Cloud Using Knn-R Algorithm

R Naga Sudha, G Nirmala


Today’s , peoples are prevalently used cloud computing platform. In this platform user can save their outlay and time by using interrogation services in cloud info. In these info sometimes data owner does not transfer in  to cloud, because information may be factotum from the malevolent users when they use in cloud if not the secure data and also secrecy of a interrogation is guaranteed. In cloud, to intensification the efficiency of interrogation processing and to save the workload of interrogation processing, it is necessary to provide secure interrogation service to user. To fully realize the benefits of cloud computing the workload must be reduced and resourceful interrogation processing must be provided. Therefore, to provide trustworthy and resourceful interrogation service RASP method is proposed, where RASP denotes Random Space Perturbation. Data Perturbation technique allows users to ascertain key summary information about the data that is not distorted and does not lead to a safe keeping breach. Exclusive safe keeping features are provided by the RASP. The RASP approach satisfies the data Trustworthyity, interrogation Secrecy, Resourceful interrogation processing and Low working outlay (CPEL) criteria for hosting queries in the cloud. KNN R algorithm is used here to process the Range interrogation to the kNN interrogation. The random space perturbation (RASP) data perturbation method to provide secure and resourceful range interrogation and kNN interrogation 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 prevailing indexing techniques to be applied to speedup range interrogation processing. The kNN R algorithm is designed to work with the RASP range interrogation algorithm to process the kNN queries.Key Words: interrogation services in the cloud, low in house processing, RASP perturbation, Range interrogation, KNN interrogation.


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