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alchemy

A Language and Compiler for Homomorphic Encryption Made easY

autotest-cpp

Sample files for clang + gtest + cmake with on-write automated test

capnproto

Cap'n Proto serialization/RPC system - core tools and C++ library

cppcnn

cryptonets

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

depthshrinker

[ICML 2022] "DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks", by Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan Lin

dotconfig

configuration in ubuntu 18.04

dotfiles

@holman does dotfiles

dotfiles-1

:wrench: .files, including ~/.macos — sensible hacker defaults for macOS

e3

E3: Encrypt-Everything-Everywhere framework for compiling C++ programs with encrypted operands.

examples

Fast and flexible reference benchmarks

ezsh

quickly install zsh, oh-my-zsh with power-level-9k zsh-completions zsh-autosuggestions zsh-syntax-highlighting history-substring-search

fourier_neural_operator

Use Fourier transform to learn operators in differential equations.

hernn

histogram

homepage_test

jyp4rk.github.io

mp-spdz

Versatile framework for multi-party computation

openfhe-cuda

This is the development repository for the OpenFHE library. The current version is 1.0.3 (released on March 17, 2023).

prezto

The configuration framework for Zsh

private-join-and-compute

simkd

[CVPR-2022] Official implementation for "Knowledge Distillation with the Reused Teacher Classifier".

stat_resnet18

tenseal

A library for doing homomorphic encryption operations on tensors

test_lattigo

tf-encrypted

A Framework for Encrypted Machine Learning in TensorFlow

zexe

Rust library for decentralized private computation