Novel Privacy-Preserving k-NN Classification Protocol Over Encrypted Data in the Cloud

Mining has wide applications in Many areas such as banking, medicine, and scientific research .Fixing is one of the nominal tasks in data drawing out applications. For the precedent decade, due to ascend a range of privacy issues, many speculative and sensible solutions to the arrangement quandary have been projected different protection models. However, users now have the occasion to subcontract their data, in encrypted form, as well as the mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing categorization techniques are not appropriate. In this paper, we focused on solving the cataloging problem over encrypted data. In particular, we proposed a secure k-NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user’s input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop secure k-NN classifier over encrypted data under the semi-honest model.