privacy-preserving-machine-learning

There are 85 repositories under privacy-preserving-machine-learning topic.

  • EthicalML/awesome-production-machine-learning

    A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

    16.2k406722.2k

  • jphall663/awesome-machine-learning-interpretability

    A curated list of awesome responsible machine learning resources.

    3.5k13260575

  • pytorch/opacus

    Training PyTorch models with differential privacy

    Language:Jupyter Notebook1.6k43284318

  • innovation-cat/Awesome-Federated-Machine-Learning

    Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

    1.6k390242

  • securefederatedai/openfl

    An open framework for Federated Learning.

    Language:Jupyter Notebook66421233179

  • LatticeX-Foundation/Rosetta

    A Privacy-Preserving Framework Based on TensorFlow

    Language:C++5542982111
  • PrivacyRaven

    trailofbits/PrivacyRaven

    Privacy Testing for Deep Learning

    Language:Python178324418

  • microsoft/robustdg

    Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.

    Language:Python16910929

  • snwagh/securenn-public

    Implementation of protocols in SecureNN.

    Language:C++12071531

  • ucbrise/piranha

    Piranha: A GPU Platform for Secure Computation

    Language:C++8621425

  • snwagh/falcon-public

    Implementation of protocols in Falcon

    Language:C++8444544

  • awslabs/fast-differential-privacy

    Fast, memory-efficient, scalable optimization of deep learning with differential privacy

    Language:Python62399

  • APPFL/APPFL

    Advanced Privacy-Preserving Federated Learning framework

    Language:Python5978113

  • DiscreetAI/decentralized-ml

    Full stack service enabling decentralized machine learning on private data

    Language:Jupyter Notebook57616

  • yamanalab/PP-CNN

    Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference

    Language:C++452113

  • ayushm-agrawal/Federated-Learning-Implementations

    This repository contains all the implementation of different papers on Federated Learning

    Language:Jupyter Notebook43405

  • FIGLAB/Vid2Doppler

    This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.

    Language:Python417115

  • sisaman/GAP

    GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)

    Language:Jupyter Notebook402911

  • leriomaggio/ppml-tutorial

    Privacy-Preserving Machine Learning (PPML) Tutorial

    Language:Jupyter Notebook35304

  • shreya-28/Secure-ML

    Secure Linear Regression in the Semi-Honest Two-Party Setting.

    Language:C++35139

  • dilawarm/federated

    Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

    Language:Python34216

  • LukasStruppek/Plug-and-Play-Attacks

    [ICML 2022 / ICLR 2024] Source code for our papers "Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks" and "Be Careful What You Smooth For".

    Language:Jupyter Notebook312107
  • responsible-ai-toolbox-privacy

    microsoft/responsible-ai-toolbox-privacy

    A library for statistically estimating the privacy of ML pipelines from membership inference attacks

    Language:Python28903

  • hharcolezi/ldp-protocols-mobility-cdrs

    Implementation of local differential privacy mechanisms in Python language.

    Language:Jupyter Notebook25106

  • JiangChSo/PFLM

    Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guarantee security: the threshold t must be greater than half of the number of users. The assumption is so rigorous that in some scenarios the schemes may not be appropriate. Motivated by the issue, we first introduce membership proof for federated learning, which leverages cryptographic accumulators to generate membership proofs by accumulating users IDs. The proofs are issued in a public blockchain for users to verify. With membership proof, we propose a privacy-preserving federated learning scheme called PFLM. PFLM releases the assumption of threshold while maintaining the security guarantees. Additionally, we design a result verification algorithm based on a variant of ElGamal encryption to verify the correctness of aggregated results from the cloud server. The verification algorithm is integrated into PFLM as a part. Security analysis in a random oracle model shows that PFLM guarantees privacy against active adversaries. The implementation of PFLM and experiments demonstrate the performance of PFLM in terms of computation and communication.

    Language:Jupyter Notebook22104

  • mmalekzadeh/privacy-preserving-bandits

    Privacy-Preserving Bandits (MLSys'20)

    Language:Jupyter Notebook22107

  • chamathpali/FedSim

    Similarity Guided Model Aggregation for Federated Learning

    Language:Python20130

  • AlanPeng0897/Defend_MI

    Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

    Language:Python19175

  • amartya18x/tapas

    Tricks for Accelerating (encrypted) Prediction As a Service

    Language:HTML19816

  • athenarc/smpc-analytics

    📊 Privacy Preserving Medical Data Analytics using Secure Multi Party Computation. An End-To-End Use Case. A. Giannopoulos, D. Mouris M.Sc. thesis at the University of Athens, Greece.

    Language:Python18403

  • Lucieno/gforce-public

    A crypto-assisted framework for protecting the privacy of models and queries in inference.

    Language:Python17302

  • inaccel/heflow

    Open source platform for the privacy-preserving machine learning lifecycle

    Language:Python16201

  • TTitcombe/NoPeekNN

    PyTorch implementation of NoPeekNN

    Language:Jupyter Notebook14334

  • barlettacarmen/CrCNN

    Crypto-Convolutional Neural Network library written on top of SEAL 2.3.1

    Language:C++13010

  • mikeroyal/Differential-Privacy-Guide

    Differential Privacy Guide

    Language:Python13301

  • D0miH/does-clip-know-my-face

    Source Code for the Paper "Does CLIP Know my Face?" (Demo: https://huggingface.co/spaces/AIML-TUDA/does-clip-know-my-face)

    Language:Jupyter Notebook11110