How to: Privacy and Safety
* [AI Gateway](https://github.com/portkey-ai/gateway) ![](https://img.shields.io/github/stars/portkey-ai/gateway.svg?cacheSeconds=86400) - The AI Gateway is a blazing fast AI Gateway with integrated guardrails.
* [ART](https://github.com/Trusted-AI/adversarial-robustness-toolbox) ![](https://img.shields.io/github/stars/Trusted-AI/adversarial-robustness-toolbox.svg?cacheSeconds=86400) - ART (Adversarial Robustness Toolbox) provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.
* [CipherChat](https://github.com/RobustNLP/CipherChat) ![](https://img.shields.io/github/stars/RobustNLP/CipherChat.svg?cacheSeconds=86400) - CipherChat is a framework to evaluate the generalization capability of safety alignment for LLMs
* [DeepTeam](https://github.com/confident-ai/deepteam) ![](https://img.shields.io/github/stars/confident-ai/deepteam.svg?cacheSeconds=86400) - DeepTeam is a simple-to-use, open-source LLM red teaming framework, for penetration testing and safe guarding large-language model systems.
* [FATE](https://github.com/FederatedAI/FATE) ![](https://img.shields.io/github/stars/FederatedAI/FATE.svg?cacheSeconds=86400) - FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy.
* [FedML](https://github.com/FedML-AI/FedML) ![](https://img.shields.io/github/stars/FedML-AI/FedML.svg?cacheSeconds=86400) - FedML provides a research and production integrated edge-cloud platform for Federated/Distributed Machine Learning at anywhere at any scale.
* [Flower](https://github.com/adap/flower) ![](https://img.shields.io/github/stars/adap/flower.svg?cacheSeconds=86400) - Flower is a Federated Learning Framework with a unified approach. It enables the federation of any ML workload, with any ML framework, and any programming language.
* [Google's Differential Privacy](https://github.com/google/differential-privacy) ![](https://img.shields.io/github/stars/google/differential-privacy.svg?cacheSeconds=86400) - This is a C++ library of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.
* [Guardrails](https://github.com/guardrails-ai/guardrails) ![](https://img.shields.io/github/stars/guardrails-ai/guardrails.svg?cacheSeconds=86400) - Guardrails is a package that lets a user add structure, type and quality guarantees to the outputs of large language models.
* [NeMo Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) ![](https://img.shields.io/github/stars/NVIDIA/NeMo-Guardrails.svg?cacheSeconds=86400) - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
* [Opacus](https://github.com/meta-pytorch/opacus)  ![](https://img.shields.io/github/stars/meta-pytorch/opacus.svg?cacheSeconds=86400) - Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment.
* [OpenFL](https://github.com/securefederatedai/openfl)  ![](https://img.shields.io/github/stars/securefederatedai/openfl.svg?cacheSeconds=86400) - OpenFL is a Python framework for Federated Learning. OpenFL is designed to be a flexible, extensible and easily learnable tool for data scientists. OpenFL is developed by Intel Internet of Things Group (IOTG) and Intel Labs.
* [PySyft](https://github.com/OpenMined/PySyft) ![](https://img.shields.io/github/stars/OpenMined/PySyft.svg?cacheSeconds=86400) - A Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party (MPC) within PyTorch.
* [Tensorflow Privacy](https://github.com/tensorflow/privacy) ![](https://img.shields.io/github/stars/tensorflow/privacy.svg?cacheSeconds=86400) - A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
* [TF Encrypted](https://github.com/tf-encrypted/tf-encrypted) ![](https://img.shields.io/github/stars/tf-encrypted/tf-encrypted.svg?cacheSeconds=86400) - A Framework for Confidential Machine Learning on Encrypted Data in TensorFlow.