-By Théo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, Jonathan Passerat-Palmbach Paper Link Secure Multiparty Computation (SMPC) is becoming increasingly popular as a way to perform operations in an untrusted environment without disclosing data. In the case of machine learning models, SMPC would protect the model weights while allowing multiple worker nodes to take part in the training phase with their own datasets, a process known as Federated Learning (FL). However, it has been shown that securely trained models are still vulnerable to reverse-engineering attacks that can extract sensitive information about the datasets directly from the model. Another set of methods, labelled as Differentially Private (DP) methods, address this and can efficiently protect the data. Abstract A new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and in...