A curated list of amazing tools, products, tutorials, applications, publications, and other resources related to OpenFHE

Numpy-like matrix arithmetic library based on OpenFHE (Work in progress)

Source code for the paper "Encrypted Image Classification with Low Memory Footprint using Fully Homomorphic Encryption"

Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents

A framework for Privacy Preserving Machine Learning

example to create python and R bindings for C++ library via CMake

Official Python wrapper for OpenFHE. Current release is v1.3.0.0 (released on May 21, 2025).

Pybind11 tool for making docstrings from C++ comments

Easily install and load the tidymodels packages

tmux source code

[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification

Demonstration of using shinytest2 to perform automated shinyloadtests

R library for converting R models to PMML

WeightedSHAP: analyzing and improving Shapley based feature attributions (NeurIPS 2022)

GNU Scientific Library with CMake build support and AMPL bindings

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Topics in Applied Machine Learning: A Hands-on Approach

A library for doing homomorphic encryption operations on tensors

VS Code in the browser

This is the development repository for the OpenFHE library. The current stable version is 1.2.4 (released on March 21, 2025). The current development version is 1.3.0 (released on May 21, 2025).

🧮 A collection of resources to learn mathematics for machine learning

Code and content for "Tidy Modeling with R"

Visualize large time series data with plotly.py

Implementation of the DGHV fully homomorphic encryption scheme

Course materials for [Statistical Inference](https://emap.fgv.br/disciplina/inferencia-estatistica)

free C++ class library of cryptographic schemes

A C++ implementation of the encryption scheme described in the paper "Fully Homomorphic Encryption over the Integers" by Marten van Dijk, Craig Gentry, Shai Halevi, Vinod Vaikuntanathan.

Powerful convenience for Julia visualizations and data analysis