Federated Learning is the current state-of-the-art in supporting secure multi-party machine
learning (ML): data is maintained on the owner's device and the updates to the model are …

Due to the strong analytical ability of big data, deep learning has been widely applied to
model on the collected data in industrial Internet of Things (IoT). However, for privacy issues …

Federated learning (FL) is an emerging paradigm that enables multiple organizations to
jointly train a model without revealing their private data to each other. This paper studies {\it …

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep
Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both …

We propose Falcon, an end-to-end 3-party protocol for efficient private training and
inference of large machine learning models. Falcon presents four main advantages-(i) It is …

In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into
Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build …

Deep learning has gained tremendous success and great popularity in the past few years.
However, deep learning systems are suffering several inherent weaknesses, which can …

Due to the complexity of the data environment, many organizations prefer to train deep
learning models together by sharing training sets. However, this process is always …

In this paper, we address the problem of privacy-preserving distributed learning and the
evaluation of machine-learning models by analyzing it in the widespread MapReduce …