Alternatives and similar repositories for jax_privacy

Users that are interested in jax_privacy are comparing it to the libraries listed below

• ftramer / Handcrafted-DP
  ☆80Updated 3 years ago
• jagielski / auditing-dpsgd
  Code for Auditing DPSGD
  ☆37Updated 3 years ago
• microsoft / prv_accountant
  A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.
  ☆73Updated last year
• lxuechen / private-transformers
  A codebase that makes differentially private training of transformers easy.
  ☆175Updated 2 years ago
• woodyx218 / opacus_global_clipping
  ☆15Updated 2 years ago
• yuxiangw / autodp
  autodp: A flexible and easy-to-use package for differential privacy
  ☆273Updated last year
• alshedivat / fedpa
  Federated posterior averaging implemented in JAX
  ☆51Updated 2 years ago
• google-research / dp-imagenet
  ☆37Updated 3 years ago
• dayu11 / Gradient-Embedding-Perturbation
  ☆27Updated 2 years ago
• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆32Updated 3 years ago
• woodyx218 / private_vision
  ☆11Updated last year
• google-research / public-data-in-dpfl
  ☆22Updated 2 years ago
• ebagdasa / differential-privacy-vs-fairness
  Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19
  ☆34Updated 4 years ago
• ChrisWaites / pyvacy
  Differentially Private Optimization for PyTorch 👁🙅‍♀️
  ☆186Updated 5 years ago
• awslabs / fast-differential-privacy
  Fast, memory-efficient, scalable optimization of deep learning with differential privacy
  ☆123Updated last month
• facebookresearch / certified-removal
  Certified Removal from Machine Learning Models
  ☆67Updated 3 years ago
• JialinMao / private_CNN
  ☆10Updated 3 years ago
• ParkLabML / DP-MERF
  ☆17Updated 2 years ago
• spring-epfl / mia
  A library for running membership inference attacks against ML models
  ☆149Updated 2 years ago
• google-research / DP-FTRL
  DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.
  ☆29Updated last month
• google-deepmind / informed_adversary_mnist_reconstruction
  ☆18Updated 3 years ago
• microsoft / dp-transformers
  Differentially-private transformers using HuggingFace and Opacus
  ☆139Updated 10 months ago
• kenziyuliu / pets-challenge
  ☆11Updated 11 months ago
• shrezaei / MI-Attack
  ☆32Updated 10 months ago
• BorjaBalle / analytic-gaussian-mechanism
  Analytic calibration for differential privacy with Gaussian perturbations
  ☆48Updated 6 years ago
• sam-yeom / ml-privacy-csf18
  Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"
  ☆37Updated 6 years ago
• wuyinjun-1993 / DeltaGrad
  ☆30Updated 3 years ago
• krishnap25 / simplicial-fl
  Simplicial-FL to manage client device heterogeneity in Federated Learning
  ☆22Updated last year
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆135Updated 2 years ago
• facebookresearch / dp_compression
  Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".
  ☆19Updated 2 years ago