A Novel Approach Based on Nearest Neighbor Search on Encrypted Databases

Abstract

Data mining services which permit designers and individuals to store their data to a server, and reduce maintenance cost. The spatial queries does not provide privacy, because the location of the query reveal sensitive information about the query. Only authorized user is allowed to access the query, even though service provider not able to view the query. This paper focuses on adducing a novel k- nearest neighbor search on the encrypted database. It describes the strong location privacy that renders a query identical in data space from any location. Due to communication cost in query processing, existing work fails to hold this search. We include a method that endeavors strong location privacy, by amalgamate Metric Preserving Transformation (MPT). Empirical results reveal that efficacy and performance of the adduced methodology has been increased and as compared to the existing methodologies