A Methodology for Training Homomorphic Encryption Friendly Neural Networks

Moran Baruch, Nir Drucker, Lev Greenberg, Guy Moshkowich

Abstract: Privacy-preserving deep neural network (DNN) inference is a necessity in different regulated industries such as healthcare, finance, and retail. Recently, homomorphic encryption (HE) has been used as a method to enable analytics while addressing privacy concerns. HE enables secure predictions over encrypted data. However, there are several challenges related to the use of HE, including DNN size limitations and the lack of support for some operation types. Most notably, the commonly used ReLU activation is not supported under some HE schemes.