Privacy-Preserving ML ML Frameworks

Libraries, frameworks, and techniques for training machine learning models while protecting data privacy through differential privacy, federated learning, secure computation, and related methods. Does NOT include general privacy policies, data governance, or non-ML privacy tools.

There are 40 privacy-preserving ml frameworks tracked. 3 score above 50 (established tier). The highest-rated is meta-pytorch/opacus at 60/100 with 1,910 stars. 2 of the top 10 are actively maintained.

Get all 40 projects as JSON

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#	Framework	Score	Tier	Stars	Language
1	meta-pytorch/opacus Training PyTorch models with differential privacy	60	Established	1,910	Jupyter Notebook
2	tensorflow/privacy Library for training machine learning models with privacy for training data	60	Established	2,003	Python
3	tf-encrypted/tf-encrypted A Framework for Encrypted Machine Learning in TensorFlow	59	Established	1,244	Python
4	awslabs/fast-differential-privacy Fast, memory-efficient, scalable optimization of deep learning with...	49	Emerging	139	Python
5	sassoftware/dpmm dpmm: a library for synthetic tabular data generation with rich...	48	Emerging	13	Python
6	IBM/differential-privacy-library Diffprivlib: The IBM Differential Privacy Library	46	Emerging	906	Python
7	privacytrustlab/ml_privacy_meter Privacy Meter: An open-source library to audit data privacy in statistical...	46	Emerging	703	Jupyter Notebook
8	Ye-D/PPML-Resource Materials about Privacy-Preserving Machine Learning	43	Emerging	263	—
9	AvinashThimmareddy/privacy-aware-data-transformation An open-source framework for automated sensitive data classification and...	42	Emerging	14	Python
10	leriomaggio/ppml-tutorial Privacy-Preserving Machine Learning (PPML) Tutorial	40	Emerging	43	Jupyter Notebook
11	wenzhu23333/Differential-Privacy-Based-Federated-Learning Everything you want about DP-Based Federated Learning, including Papers and...	40	Emerging	423	Python
12	NervanaSystems/he-transformer nGraph-HE: Deep learning with Homomorphic Encryption (HE) through Intel nGraph	39	Emerging	223	C++
13	Guyanqi/Awesome-Privacy Repository for collection of research papers on privacy.	39	Emerging	344	—
14	filrg/split_learning スプリットラーニング - Split Learning with PyTorch	39	Emerging	7	Python
15	nesaorg/nesa Run AI models end-to-end encrypted.	37	Emerging	3,070	Python
16	deepanwadhwa/zink A Python package for zero-shot text anonymization using Transformer-based NER models.	37	Emerging	82	Python
17	sisaman/GAP GAP: Differentially Private Graph Neural Networks with Aggregation...	35	Emerging	49	Jupyter Notebook
18	jimouris/curl Curl: Private LLMs through Wavelet-Encoded Look-Up Tables	32	Emerging	16	Python
19	Shuyib/data-privacy-pres A repo that takes you through some principles about data privacy based on...	31	Emerging	7	HTML
20	privacy-tech-lab/privacy-pioneer-machine-learning Code and models for the machine learning used in Privacy Pioneer	31	Emerging	10	Jupyter Notebook
21	eth-sri/dp-sniper A machine-learning-based tool for discovering differential privacy...	30	Emerging	24	Python
22	JeffffffFu/Awesome-Differential-Privacy-and-Meachine-Learning Differentially private federated learning: A systematic review (ACM Survey);...	29	Experimental	383	Python
23	sisaman/ProGAP ProGAP: Progressive Graph Neural Networks with Differential Privacy...	28	Experimental	6	Jupyter Notebook
24	DominiqueMercier/PPML-TSA Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures:...	28	Experimental	6	Jupyter Notebook
25	ar-roy/dct-cryptonets Official code for "DCT-CryptoNets: Scaling Private Inference in the...	27	Experimental	10	Python
26	VectorInstitute/privacy-enhancing-techniques A collection of demos and utilities prepared ahead of the Vector Institute...	24	Experimental	15	Jupyter Notebook
27	azithteja91/phi-exposure-guard Adaptive PHI de-identification for streaming multimodal data:...	22	Experimental	—	Python
28	Crypto-TII/FANNG-MPC Your GoTo Library for NN's over MPC	22	Experimental	7	Verilog
29	williamdevena/Defending-federated-learning-system Implementation of a client reputation, gradient checking and homomorphic...	22	Experimental	17	Python
30	loretanr/dp-gbdt GBDT learning + differential privacy. Standalone C++ implementation of...	22	Experimental	9	C++
31	hsp1234h/openpcc 🔒 Achieve privacy in AI inference with OpenPCC, an open-source framework for...	22	Experimental	—	Go
32	simran-arora/focus This repo contains code for the paper: "Can Foundation Models Help Us...	20	Experimental	24	Python
33	AdityaBhatt3010/DP-SGD-Differential-Privacy-Stochastic-Gradient-Descent Differential Privacy, DP-SGD, MNIST — Comparative analysis of...	20	Experimental	1	Jupyter Notebook
34	khoaguin/ppml-materials A compiled list of resources and materials for PPML	15	Experimental	11	—
35	kenziyuliu/DP2 [ICLR 2023] Official JAX/Haiku implementation of the paper "Differentially...	15	Experimental	16	Python
36	miguelfrndz/Differential-Privacy-GL-Attacks Differential Privacy: Gradient Leakage Attacks in Federated Learning Environments	14	Experimental	4	Jupyter Notebook
37	mikeroyal/Differential-Privacy-Guide Differential Privacy Guide	12	Experimental	20	Python
38	kmoonn/Privacy-Preserving-Deep-Learning 面向隐私保护深度学习的变换数据分类方法	11	Experimental	—	Jupyter Notebook
39	Dustin-Ray/capy2vML Trains a differentially-private linear regression inside of the RISC-Zero...	11	Experimental	2	Rust
40	guilhermecerqueiraoliveira/PyPrivacy PyPrivacy é uma ferramenta desenvolvida em Python com o objetivo de ocultar...	10	Experimental	1	Python

Comparisons in this category

privacy and differential-privacy-library (60 vs 46) PPML-Resource and ppml-tutorial (43 vs 40)