Before revision:  \\%DIF > and classification took less than 3 hours compared to (estimated) weeks in current HE implementations.
After revision:  When implemented with HE, the k-ish NN has a circuit depth that is independent of n, therefore making it scalable. We also implemented our classifier in an open source library based on HELib and tested it on a breast tumor database. The accuracy of our classifier (F_1 score) were 98\\%DIF > and classification took less than 3 hours compared to (estimated) weeks in current HE implementations.