Encrypted Inference using ResNet-18

Encrypted inference with ResNet-18 using PyTorch + PySyft on ants & bees images

CrypTen Integration into PySyft

CrypTen integrated in PySyft: a fast SMPC backend for secure computation between servers.

Sentiment Analysis on Multiple Datasets With SyferText - Demo

How can you do pre-processing if you are not allowed to have access to plaintext data? SyferText can help you! With SyferText, you can define pre-processing components to perform pre-processing remotely, blindly and in a completely secure fashion.

What is Federated Learning?

This post is part of our Privacy-Preserving Data Science, Explained series. In this article of the introductory series on Private ML, we wi

Build an Homomorphic Encryption Scheme from Scratch with Python

This blog post aims at explaining the basic mathematical concepts behind most of today's homomorphic encryption schemes, and then build upon this to implement our own scheme (similar to BFV) from scratch using Python.

PySyft, PyTorch and Intel SGX: Secure Aggregation on Trusted Execution Environments

When we talk about sensitive data and cloud computing, how can we guarantee the remote, secure and private execution of our applications? One possible solution to this problem is to run the application in a Trusted Execution Environment (TEE).

Introduction to Federated Learning and Privacy Preservation using PySyft and PyTorch

Federated Learning and Additive Secret Sharing using the PySyft framework

Encrypted Training with PyTorch + PySyft

Encrypted Training of Deep Learning models with PyTorch + PySyft on MNIST

Federated Learning of a Recurrent Neural Network on Raspberry PIs

In this article, you are going to learn how to setup PySyft on a Raspberry PI and how to train a Recurrent Neural Network in a federated way.

Encrypted Deep Learning Classification with PyTorch & PySyft

Encrypted Deep Learning Classification with PyTorch & PySyft in < 33ms on MNIST

Training a CNN using SPDZ

An implementation of Convolutional Neural Networks (CNNs) using secure Multi-Party Computation (MPC).