google-deepmind / jax_privacyLinks
Algorithms for Privacy-Preserving Machine Learning in JAX
☆107Updated last week
Alternatives and similar repositories for jax_privacy
Users that are interested in jax_privacy are comparing it to the libraries listed below
Sorting:
- ☆80Updated 3 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆74Updated last year
- Code for Auditing DPSGD☆37Updated 3 years ago
- ☆15Updated 2 years ago
- A codebase that makes differentially private training of transformers easy.☆177Updated 2 years ago
- Fast, memory-efficient, scalable optimization of deep learning with differential privacy☆134Updated 3 months ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆32Updated 3 years ago
- autodp: A flexible and easy-to-use package for differential privacy☆275Updated last year
- ☆11Updated 2 years ago
- ☆37Updated 3 years ago
- ☆18Updated 3 years ago
- Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy☆18Updated last year
- Breaching privacy in federated learning scenarios for vision and text☆307Updated 2 months ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆33Updated 5 months ago
- A library for running membership inference attacks against ML models☆152Updated 2 years ago
- ☆27Updated 2 years ago
- Private Evolution: Generating DP Synthetic Data without Training [ICLR 2024, ICML 2024 Spotlight]☆107Updated this week
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆135Updated 2 years ago
- Certified Removal from Machine Learning Models☆69Updated 4 years ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆33Updated 4 years ago
- ☆58Updated 5 years ago
- ☆23Updated 2 years ago
- ☆10Updated 3 years ago
- Differentially-private transformers using HuggingFace and Opacus☆143Updated last year
- ☆32Updated last year
- R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]☆37Updated 2 years ago
- ☆11Updated last year
- ☆24Updated 3 years ago
- Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".☆19Updated 2 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆50Updated 7 years ago