Tutorial - Privacy-Preserving Machine and Deep Learning with Homomorphicencryption: an Introduction

Manuel Roveri, Politecnico di Milano (ITALY); Alessandro Falcetta, Politecnico di Milano (ITALY) ABSTRACT:Privacy-preserving machine and deep learning with homomorphic encryption (HE) is a novel and promising research area aiming at designing machine and deep learning solutions able to operate while guaranteeing the privacy of user data. Designing privacy-preserving machine and deep learning solutions requires completely rethinking and redesigning machine and deep learning models and algorithms to match the severe technological and algorithmic constraints of HE. The aim of this tutorial is to provide an introduction to such a complex yet challenging research area by also providing tools and solutions for the design of privacy-preserving convolutional neural networks (CNNs). This tutorial will provide both an algorithmic and technological perspective on this field by integrating theory with a practical coding session in Python. Furthermore, research challenges and available software resources for privacy-preserving deep learning with HE will be commented and described.