Secure Optimal k-NN on Encrypted Cloud Data using Homomorphic Encryption with Query Users

Abstract

In cloud computing, research on security issues among outsourced encrypted data is trending topic. It has broad applications in area-based management, classification, and clustering. As any other normal utilized query for online applications, secure k-Nearest Neighbors (k-NN) calculation on encrypted cloud data is highly being considered now a days, and a few advanced answers have been produced. This paper proposed an innovative plan for encrypting the outsourced database and query points. The new plan can adequately support k-Nearest Neighbor (KNN) computation while preserving data privacy and query privacy. To improve the performance of the system, the Opposition-based Particle Swarm Optimization (OPSO) optimization algorithm is utilized to secure the data by Homomorphic Encryption (HE) method. The broad hypothetical and test assessments exhibit the adequacy of our plan with regards to security and performance.