Volume 16 - Issue 1
Multidimensional Range Query over Encrypted Data
Abstract
In various circumstances, as a model sql recovers territorial organizations and numerical methods, a critical research expansion request has been treated. Then with precarious data volume advancement customers are logically inclined to store cloud data to save accumulation and computing costs in the neighborhood. In any case, a long-standing problem is that the customer's data can be fully disclosed to the web server since it has complete data finding a workable pace. To adjust to this issue a routinely used method is to encode unrefined data before re-appropriating it anyway, the openness and operability of the data on a very basic level will be diminished. Currently, propose a persuasive and graphical query plot egrq supporting look and data consider mixed spatial data workable speed. We use secure knn computing polynomial fitting methodology and request defending encryption to achieve secure, capable and precise requests for mathematical range over cloud data. Until then we're proposing a new spatial data to find a good pace in our egrq to optimize consumer benefits. To boost the adequacy of the r-tree, the glimpse through space is reduced and events are arranged in the entire chase cycle. Finally, we optimistically exhibit the security of implemented configuration to the extent of the mystery of spatial data insurance record and stairway confirmation and trapdoor unlikelihood. Wide-ranging reviews likewise demonstrate the high capacity of our proposed model, differentiated and existing.
Paper Details
PaperID: 201010
Author's Name: Ch. Tejaswini, K. Praveen Kumar and L.V. Kiran
Volume: Volume 16
Issues: Issue 1
Keywords: Spatial Data, Range Query, Privacy protection, R-tree, Access control.
Year: 2020
Month: February
Pages: 58-64