Exploiting RASP Data Perturbation to BuildConfidential Query Services in the Cloud

Abstract

With the advent of cloud computing technology, using clouds for hosting data query service has become increasingly popular because of the low cost of computation, hosting applications and content storage. As in cloud the data management and infrastructure management is provided by third-party security and privacy are the biggest concern. Until and unless data confidentiality and secure query processing are guaranteed it is always a risk for the data owner to move the sensitive data to the cloud. Workload must be reduced to fully realize the benefits of cloud computing. Therefore to meet the above said requirements RASP method is proposed where RASP stands for Random Space Perturbation. This data perturbation technique ensures that the data is not d istorted and does not lead to a security breach by allowing users to ascertain key summary information. Cloud computing enables outsourcing the management of the data related to individuals and organizations to a service provider as the hardware cost and t he maintenance cost is less. RASP provides exclusive security features for hosting query services in the cloud by satisfying the CPEL criteria where CPEL stands for data Confidentiality, query Privacy, Efficient query processing and Low working cost .KNN-R algorithm is used to process the range query and the KNN query. Here users have been authorized by the randomly generated product key value provided by the admin after successful registration followed by activation by admin thus maintaining confidentiality. User queries are retrieved within a very short span of time. Also analysed how the RASP method provides confidentiality of data and increases the working process of query.