A Survey on k-NN Classification over Semantically Secure Encrypted Relational

Data Mining has wide applications in numerous zones, for example, keeping money, prescription, investigative exploration and among government offices. Order is one of the ordinarily utilized assignments as a part of information mining applications. For as far back as decade, due to the ascent of different protection issues, numerous hypothetical and commonsense answers for the order issue have been proposed under diverse security models. Notwithstanding, with the late fame of distributed computing, clients now have the chance to outsource their information, in encoded structure, and also the information mining assignments to the cloud. Since the information on the cloud is in encoded structure, existing security protecting characterization methods are not appropriate. In this paper, we concentrate on fathoming the characterization issue over encoded information. Specifically, we propose a safe k-NN classifier over scrambled information in the cloud. The proposed convention ensures the classification of information, security of client's data inquiry, and shrouds the information access designs. To the best of our learning, our work is the first to add to a safe k-NN classifier over scrambled information under the semi-legitimate model. Additionally, we exactly dissect the effectiveness of our proposed convention utilizing a genuine dataset under diverse parameter settings.