Erection Trusted and Effective Request Services in the Cloud with RASP Data Perturbation

Range query is one of the most frequently used queries for online data analytics. Providing such a query service could be expensive for the data owner. With the development of services computing and cloud computing, it has become possible to outsource large databases to database service providers and let the providers maintain the range-query service. With outsourced services, the data owner can greatly reduce the cost in maintaining computing infrastructure and data-rich applications. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. 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. We use RASP to generate indexable auxiliary data that is resilient to prior knowledge enhanced attacks. Range queries are securely transformed to the encrypted data space and then efficiently processed with a two-stage processing algorithm. Index TermsRASP, query services in the cloud, privacy, range query, kNN query