Efficient Computation of Reverse Skyline Queries
Abstract
Outsourcing data to cloud server provides better usability of data. But to preserve the security and privacy these outsourced data need to be protected from the cloud server and other unauthorized users. The encryption of data can protect the data. For query processing, the skyline query is used and it is particularly important for multi criteria decision making. As a part of the work, the work can be categorized into four parts mainly. And they are Data Transformation, where data to be transformed into different form by performing RSA encryption and the noise appending process where random noise values are generated and appended to the data. Then comes the Auditing where it checks whether there is an occurrence of attack or not. Skyline Computations specifies the Secure Skyline computations over the encrypted data. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Furthermore, we demonstrate two optimizations, data partitioning and lazy merging, to further reduce the computation load. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.
