John C. Smith 's repositories
ExRoc.BigInteger
Provides a BigInteger class that can be used like a basic data type https://github.com/ExRoc/BigInteger/
HElib
HElib is an open-source software library that implements homomorphic encryption. It supports the BGV scheme with bootstrapping and the Approximate Number CKKS scheme. HElib also includes optimizations for efficient homomorphic evaluation, focusing on effective use of ciphertext packing techniques and on the Gentry-Halevi-Smart optimizations.
Agenda
PLANS in CALENDER
stat453-deep-learning-ss21
STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2021)
SecureML_Ref
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
pytorchTutorial
PyTorch Tutorials from my YouTube channel
CryptoDL
Privacy-preserving Deep Learning based on homomorphic encryption (HE)
MuchPIR
Homomorphic Encryption PIR Postgres C/C++ Agregate Extension.
lattigo
A library for lattice-based homomorphic encryption in Go
TenSEAL
A library for doing homomorphic encryption operations on tensors
deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Machine-Learning-Collection
PyTorch Tutorials, TensorFlow Tutorials and Machine Learning Algorithms
tensorflow-course
Tensorflow Beginner Course from my YouTube channel
concrete
Concrete Operates oN Ciphertexts Rapidly by Extending TfhE
anonymous_github
Anonymous Github is a proxy server to support anonymous browsing of Github repositories for open-science code and data.
ring-perisic-com
which is a package of Java classes for multivariate and univariate polynomials over the rings: Z(integers), Q (rational numbers), R (real numbers), C (complex numbers), F_2 (field of two elements), Cyclotomic Number Fields, and more. Also supported are modular rings K[X]/f(X) for a ring K and a polynomial f and Quotient Fields over rings. There are
core
MIRACL Core
cs230-code-examples
Code examples in pyTorch and Tensorflow for CS230
SEAL
Microsoft SEAL is an easy-to-use and powerful homomorphic encryption library.
awesome-vpn
A curated list of awesome free VPNs and proxies.免费的代理,科学上网,翻墙，梯子大集合
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.
HEMat
Homomorphic matrix computation