Efficient homomorphic evaluation of <i>k</i>-NN classifiers

Abstract We design and implement an efficient, secure, homomorphic k-Nearest Neighbours determination algorithm, to be used for regression or classification over private data. Our algorithm runs in quadratic complexity with regard the size of database but is only one literature make secure completely non-interactively. show that our both efficient accurate when applied problems requiring a small set model vectors, still scales larger sets vectors high accuracy yet at greater (sequential) computational costs.