Build Efficient Query Services in the Cloud with RASP and Range Queries

Abstract
Cloud computing is mainly used to store and retrieve data and also to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, Encryption is a well-established technology for protecting sensitive data. When data encrypted once, data can no longer be easily queried aside from exact matches .A secured query service reduce the in-house workload to fully realize the benefits of cloud computing. The RASP data perturbation and kNN query services method provide secure range query and kNN query services for protected data in the cloud. Continuous K nearest neighbor queries (C-KNN) is defined as the nearest points of interest to all the points on a path. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the queries. The attacks on data and queries under a precisely defined threat model and realistic security assumptions are analyzed. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.
INDEX TERMS:query services in the cloud, privacy, range query, kNN query.
I.Introduction
Hosting data-intensive query services in the cloud is increasing now a day. The unique advantages in cloud is scalability and cost-saving. With the cloud Infrastructures, the service owners can only pay for the hours of using the servers. It will be expensive and inefficient to serve dynamic workloads with in-house infrastructures [2]. The service providers can lose the control over the data in the cloud. Adversaries, such as curious service Providers can possibly make a copy of the database or eaves drop users’ queries, which will be difficult to detect and prevent in the cloud infrastructures. The purpose of using cloud resources is to reduce the need of maintaining scalable in-house infrastructures. Therefore, there is an intricate relationship among the data confidentiality, query Privacy, the quality of service, we propose the Random Space Perturbation (RASP) approach to constructing practical range query and k-nearest-neighbor (kNN) query services in the cloud. The RASP kNN query service (kNN-R) uses the RASP range query service to process kNN queries. RASP has several important features. RASP does not preserve the order of dimensional values and thus does not suffer from the distribution-based attack. The Knn query service is to find the nearest places. Order preserving encryption (OPE) scheme maps a set of single-dimensional values to another, while keeping the value order unchanged. The RASP perturbation is a unique combination of OPE. The main scope of the project is to host data query services using clouds and build efficient query services with data perturbation method. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the queries that stored in the cloud. The rest of the paper is organized as follows- Section II is about some existing methodologies proposed for partially observable system. In Section III, the proposed Color Pass scheme has been discussed in detail. The user interface for Color Pass has been described in Section IV. Finally we conclude in Section VI and give future direction of our work..