Secure kNN Computation and Integrity Assurance of Data Outsourcing in the Cloud

Abstract

As cloud computing has been popularized massively and rapidly, individuals and enterprises prefer outsourcing their databases to the cloud service provider (CSP) to save the expenditure for managing and maintaining the data. The outsourced databases are hosted, and query services are offered to clients by the CSP, whereas the CSP is not fully trusted. Consequently, the security shall be violated by multiple factors. Data privacy and query integrity are perceived as two major factors obstructing enterprises from outsourcing their databases. A novel scheme is proposed in this paper to effectuate -nearest neighbors (kNN) query and query authentication on an encrypted outsourced spatial database. An asymmetric scalar-product-preserving encryption scheme is elucidated, in which data points and query points are encrypted with diverse encryption keys, and the CSP can determine the distance relation between encrypted data points and query points. Furthermore, the similarity search tree is extended to build a novel verifiable SS-tree that supports efficient query and query verification. It is indicated from the security analysis and experiment results that our scheme not only maintains the confidentiality of outsourced confidential data and query points but also has a lower query processing and verification overhead than the MR-tree.

Cloud based multicasting using fat tree data confidential recurrent neural network

“…Asymmetric scalar-product-preserving encryption scheme was studied in [16], to control the privacy of data. Successful processing of data and analysis for Geological Information Services was addressed in [17] with the aid of aggregation.…”